WEBVTT 00:00:04.879 --> 00:00:06.500 position:50% align:middle - [Richard] Okay. So, greetings. Hello. 00:00:06.500 --> 00:00:15.740 position:50% align:middle I'm here to present the results from the 2022 National Nursing Workforce Survey. 00:00:15.740 --> 00:00:20.370 position:50% align:middle And in this presentation, I will go through the background and methods of the 00:00:20.370 --> 00:00:24.230 position:50% align:middle survey and present the response rates. 00:00:24.230 --> 00:00:33.060 position:50% align:middle I will then review the results from the RN and LPN/LVN surveys and present a quick look at the future. 00:00:33.060 --> 00:00:37.690 position:50% align:middle I will answer questions after the presentation has ended. 00:00:37.690 --> 00:00:45.020 position:50% align:middle And just a quick note, I will be referring to LPN/LVNs as just LPNs for the 00:00:45.020 --> 00:00:49.490 position:50% align:middle sake of brevity. 00:00:49.490 --> 00:00:54.320 position:50% align:middle So, as a background, this survey is the result of a collaborative 00:00:54.320 --> 00:00:59.310 position:50% align:middle partnership with the National Forum of State Nursing Workforce Centers. 00:00:59.310 --> 00:01:04.750 position:50% align:middle Since the 1970s, nursing supply data had been collected every four years by HRSA, 00:01:04.750 --> 00:01:11.780 position:50% align:middle the Health Resources and Services Administration, via their National Sample Survey of Registered Nurses. 00:01:11.780 --> 00:01:18.850 position:50% align:middle But after the 2008 survey was conducted, it was announced that a 2012 survey would not be 00:01:18.850 --> 00:01:22.580 position:50% align:middle conducted due to the lack of funding. 00:01:22.580 --> 00:01:29.880 position:50% align:middle NCSBN stepped up in 2013 to fill the void in the RN supply data by conducting the National 00:01:29.880 --> 00:01:31.890 position:50% align:middle Nursing Workforce Survey. 00:01:31.890 --> 00:01:39.628 position:50% align:middle The survey was conducted again in 2015 with LPNs included and was subsequently conducted 00:01:39.628 --> 00:01:45.074 position:50% align:middle in 2017, 2020, and now 2022. 00:01:45.074 --> 00:01:56.070 position:50% align:middle And I will note here that the 2022 survey serves as the largest, most rigorous, 00:01:56.070 --> 00:02:03.570 position:50% align:middle and comprehensive study of the U.S. nursing workforce since the onset of COVID-19 pandemic. 00:02:03.570 --> 00:02:13.760 position:50% align:middle And the report that follows will focus on the pandemic's impact on the nursing workforce. 00:02:13.760 --> 00:02:20.530 position:50% align:middle For the 2022 survey, a mixed modes approach was used to collect the data. 00:02:20.530 --> 00:02:25.800 position:50% align:middle The data for 43 jurisdictions were captured through a direct mail survey administered 00:02:25.800 --> 00:02:28.220 position:50% align:middle by a third-party vendor. 00:02:28.220 --> 00:02:33.800 position:50% align:middle For four jurisdictions, an email survey using Qualtrics was employed. 00:02:33.800 --> 00:02:38.300 position:50% align:middle For five jurisdictions, data were collected internally 00:02:38.300 --> 00:02:40.880 position:50% align:middle from our e-Notify system. 00:02:40.880 --> 00:02:46.810 position:50% align:middle In summary, data were collected for all 50 states, the District of Columbia, 00:02:46.810 --> 00:02:49.760 position:50% align:middle and the Northern Mariana Islands. 00:02:49.760 --> 00:02:57.530 position:50% align:middle In collecting the mailout sample, all active RN and LPN licensees were eligible 00:02:57.530 --> 00:03:00.330 position:50% align:middle for survey participation. 00:03:00.330 --> 00:03:06.360 position:50% align:middle The sample was stratified by state, and the survey was mailed to over 150,000 RNs 00:03:06.360 --> 00:03:10.340 position:50% align:middle and 150,000 LPNs. 00:03:10.340 --> 00:03:18.300 position:50% align:middle Likewise, in collecting the email sample, all active RN and LPN licensees were eligible 00:03:18.300 --> 00:03:20.580 position:50% align:middle for survey participation. 00:03:20.580 --> 00:03:27.710 position:50% align:middle That sample was stratified by state, and over 25,000 RNs and 18,000 LPNs were selected to be 00:03:27.710 --> 00:03:33.220 position:50% align:middle sent the survey. 00:03:33.220 --> 00:03:39.790 position:50% align:middle All of the RNs and LPNs captured by the e-Notify system were included in the study. 00:03:39.790 --> 00:03:47.740 position:50% align:middle The data collected via this method had undergone an extensive review, and a determination was made that the 00:03:47.740 --> 00:03:54.590 position:50% align:middle data were sufficiently comparable to the workforce data previously collected in the selected jurisdictions so 00:03:54.590 --> 00:04:00.160 position:50% align:middle that a separate workforce survey was unnecessary. 00:04:00.160 --> 00:04:08.010 position:50% align:middle In composing the survey, the form's minimum data supply set was used to form the 00:04:08.010 --> 00:04:09.910 position:50% align:middle bulk of the questions. 00:04:09.910 --> 00:04:15.520 position:50% align:middle Additional questions on the survey were asked about telehealth, the National Licensure Compact, 00:04:15.520 --> 00:04:21.280 position:50% align:middle future retirement, travel nursing, and direct patient care. 00:04:21.280 --> 00:04:27.160 position:50% align:middle Also, the 2022 survey added questions about the COVID-19 pandemic. 00:04:27.160 --> 00:04:37.860 position:50% align:middle Please note that the e-Notify jurisdictions only collected the MDS data set questions. 00:04:37.860 --> 00:04:43.500 position:50% align:middle After all the responses were in, a nonresponse bias analysis was conducted to evaluate 00:04:43.500 --> 00:04:47.370 position:50% align:middle survey response patterns by age and gender. 00:04:47.370 --> 00:04:52.010 position:50% align:middle Weights were created, which adjusted for nonresponse by age and gender and 00:04:52.010 --> 00:04:57.440 position:50% align:middle adjusted for the stratification by state in the original survey design. 00:04:57.440 --> 00:05:07.360 position:50% align:middle So those weights were applied to the subsequent descriptive analysis that is being presented here. 00:05:07.360 --> 00:05:14.590 position:50% align:middle Nearly 27,000 RNs and nearly 23,000 LPNs responded to the mailout survey. 00:05:14.590 --> 00:05:19.172 position:50% align:middle Response rates were 18% for RNs and 15% for LPNs. 00:05:19.172 --> 00:05:26.880 position:50% align:middle The RN and LPN email surveys each received between 2,000 and 2,500 responses. 00:05:26.880 --> 00:05:31.747 position:50% align:middle The response rates were 9% for the RNs and 12% for the LPNs. 00:05:31.747 --> 00:05:43.470 position:50% align:middle From the e-Notify jurisdictions, 250,000 RN records and 30,000 LPN records were used. 00:05:43.470 --> 00:05:51.370 position:50% align:middle And we now will proceed to the results for the registered nurse's portion of the survey. 00:05:51.370 --> 00:05:58.410 position:50% align:middle The RN nursing workforce underwent a dramatic shift in the wake of the COVID-19 pandemic. 00:05:58.410 --> 00:06:05.250 position:50% align:middle Many nurses who are in the older age ranges in 2020 left the nursing workforce, 00:06:05.250 --> 00:06:12.410 position:50% align:middle and that resulted in a decline in the median RN age of the workforce by six years. 00:06:12.410 --> 00:06:17.030 position:50% align:middle And just as a side note, this is a dramatic shift. 00:06:17.030 --> 00:06:21.860 position:50% align:middle All the time I've been doing the survey, I'm used to seeing the median age either stay the same 00:06:21.860 --> 00:06:25.180 position:50% align:middle or shift up by a year or shift down by a year. 00:06:25.180 --> 00:06:31.467 position:50% align:middle For it to change by six years in two years is unprecedented. 00:06:31.467 --> 00:06:37.715 position:50% align:middle Almost a quarter of the RN workforce is now aged 34 or younger. 00:06:37.715 --> 00:06:45.210 position:50% align:middle In 2020, nurses aged 55 and older accounted for 43% of the RN workforce. 00:06:45.210 --> 00:06:51.440 position:50% align:middle Now, in 2022, the same age cohort accounted for 31% of the RN workforce. 00:06:51.440 --> 00:07:04.020 position:50% align:middle This decline was associated with estimated losses to the workforce of at least 200,000 experienced RNs. 00:07:04.020 --> 00:07:08.780 position:50% align:middle The women continue to account for a very large majority of nurses. 00:07:08.780 --> 00:07:16.140 position:50% align:middle The proportion of men licensed as RNs in the country has increased steadily since at least 2015. 00:07:16.140 --> 00:07:23.614 position:50% align:middle Currently, men account for 11% of the RN workforce, which is up from 8% in 2015. 00:07:23.614 --> 00:07:35.380 position:50% align:middle RNs are more likely to report identifying as an underrepresented racial minority than they were before. 00:07:35.380 --> 00:07:40.860 position:50% align:middle Overall, 24% of RNs reported being in a racial or ethnic minority in 2022, 00:07:40.860 --> 00:07:45.850 position:50% align:middle which is a slight increase over the 23% reported in 2020. 00:07:45.850 --> 00:07:51.580 position:50% align:middle In contrast, the Census Bureau reports that 41% of the U.S. 00:07:51.580 --> 00:07:56.740 position:50% align:middle population in 2021 were in racial or ethnic minority groups. 00:07:56.740 --> 00:08:03.400 position:50% align:middle RNs who reported being of Hispanic or Latino origin composed 7% of the workforce in 2022, 00:08:03.400 --> 00:08:09.010 position:50% align:middle as opposed to 4% in 2015. 00:08:09.010 --> 00:08:16.280 position:50% align:middle Levels of educational accomplishment among RNs continue to increase. 00:08:16.280 --> 00:08:23.490 position:50% align:middle In the 2022 survey, 47% of the RNs held a baccalaureate degree as their initial nursing education, 00:08:23.490 --> 00:08:29.630 position:50% align:middle while over 70% of the workforce reported holding a baccalaureate degree or higher as their highest 00:08:29.630 --> 00:08:31.970 position:50% align:middle degree of education. 00:08:31.970 --> 00:08:39.950 position:50% align:middle This sharp increase in educational attainment is partially due to the loss of the workforce of many 00:08:39.950 --> 00:08:49.820 position:50% align:middle older RNs who had not earned a baccalaureate as their initial education. 00:08:49.820 --> 00:08:57.030 position:50% align:middle The COVID-19 pandemic had a notable impact on RN workforce employment. 00:08:57.030 --> 00:09:00.000 position:50% align:middle Eighty-nine percent of the RN licensees were actively employed in nursing. 00:09:00.000 --> 00:09:04.202 position:50% align:middle This is an increase from 84% in 2020. 00:09:04.202 --> 00:09:13.504 position:50% align:middle And 70% of RN licensees were working full-time, which represented an increase from 65% in 2020. 00:09:13.504 --> 00:09:21.070 position:50% align:middle Post-pandemic inflation is reflected in RN salaries. 00:09:21.070 --> 00:09:33.210 position:50% align:middle Median pre-tax earnings rose from 70,000 in 2020 to 80,000 in 2022. 00:09:33.210 --> 00:09:41.330 position:50% align:middle Nurses were asked to indicate the percentage of time they provided nursing services or communicated with a 00:09:41.330 --> 00:09:48.350 position:50% align:middle patient or client located somewhere different from where they were located, via phone or electronically. 00:09:48.350 --> 00:09:53.430 position:50% align:middle About half of the RN workforce reported being engaged in such activities. 00:09:53.430 --> 00:10:00.770 position:50% align:middle This proportion is similar to numbers reported in previous years. 00:10:00.770 --> 00:10:08.170 position:50% align:middle Of those RNs providing services remotely, proportions reported providing services over state and 00:10:08.170 --> 00:10:17.920 position:50% align:middle national borders remain constant in comparison to previous years. 00:10:17.920 --> 00:10:27.189 position:50% align:middle Of those RNs providing nursing services remotely, usage of video calls tripled from 2020 to 2022. 00:10:27.189 --> 00:10:35.910 position:50% align:middle The usage of electronic messaging also increased. 00:10:35.910 --> 00:10:40.590 position:50% align:middle Nurses were asked if they hold a multi-state license. 00:10:40.590 --> 00:10:46.840 position:50% align:middle Among RNs who hold a multi-state license, over two-thirds have not used it, 00:10:46.840 --> 00:10:51.040 position:50% align:middle but another way of looking at this is about one-third have. 00:10:51.040 --> 00:10:54.910 position:50% align:middle So that's good. 00:10:54.910 --> 00:10:59.370 position:50% align:middle Now, as I mentioned, additional questions were asked in this year's survey, 00:10:59.370 --> 00:11:07.840 position:50% align:middle which specifically focused on the impact of COVID-19 on the workforce and on the respondents themselves. 00:11:07.840 --> 00:11:14.880 position:50% align:middle Sixty-two percent of RNs reported that their workload increased, 16% reported that they changed their 00:11:14.880 --> 00:11:21.660 position:50% align:middle practice setting as a result of the pandemic, and 9% reported that they left or retired nursing as a 00:11:21.660 --> 00:11:23.530 position:50% align:middle result of the pandemic. 00:11:23.530 --> 00:11:33.050 position:50% align:middle In addition, 46% reported that they felt burnt out at least a few times a week as a result of the pandemic. 00:11:33.050 --> 00:11:41.350 position:50% align:middle And now I'm going to move on to the LPN results. 00:11:41.350 --> 00:11:47.970 position:50% align:middle The LPN nursing workforce also underwent a dramatic shift in the wake of the COVID-19 pandemic. 00:11:47.970 --> 00:11:53.550 position:50% align:middle Many nurses who were in the older age range in 2020 left the workforce, resulting, once again, 00:11:53.550 --> 00:11:57.280 position:50% align:middle in a decline in the median LPN age of six years. 00:11:57.280 --> 00:12:04.300 position:50% align:middle In 2020, nurses aged 55 and older accounted for 42% of the LPN workforce. 00:12:04.300 --> 00:12:10.950 position:50% align:middle In 2022, this same age cohort accounted for 30% of the LPNs. 00:12:10.950 --> 00:12:21.180 position:50% align:middle This decline was associated with estimated losses to the workforce of at least 60,000 experienced LPNs. 00:12:21.180 --> 00:12:26.750 position:50% align:middle And though women continue to account for a very large majority of nurses, the proportion of men licensed 00:12:26.750 --> 00:12:31.520 position:50% align:middle as LPNs in the country has increased steadily since at least 2015. 00:12:31.520 --> 00:12:36.830 position:50% align:middle Currently, men account for 10% of the LPN workforce, up from 8% in 2015. 00:12:36.830 --> 00:12:49.630 position:50% align:middle LPNs are also more likely to report identifying as an underrepresented racial minority, 00:12:49.630 --> 00:12:51.650 position:50% align:middle in comparison to previous years. 00:12:51.650 --> 00:13:03.540 position:50% align:middle Overall, 40% of LPNs reported being in a racial/ethnic minority in 2022, an increase over the 36% reported 00:13:03.540 --> 00:13:13.185 position:50% align:middle in 2020 and almost matching the 41% that the Census Bureau reports being in the U.S. population. 00:13:13.185 --> 00:13:18.970 position:50% align:middle So for the LPN workforce, that aspect of diversity almost matches what's 00:13:18.970 --> 00:13:25.495 position:50% align:middle in the workforce, although the distribution across various minorities is a little different than what's 00:13:25.495 --> 00:13:27.497 position:50% align:middle in the U.S. census. 00:13:27.497 --> 00:13:36.620 position:50% align:middle LPNs also who reported being of Hispanic or Latino origin composed 12% of the workforce in 2022, 00:13:36.620 --> 00:13:41.200 position:50% align:middle as opposed to 6% in 2015. 00:13:41.200 --> 00:13:48.360 position:50% align:middle Levels of educational accomplishment among LPNs have increased in the 2022 survey. 00:13:48.360 --> 00:13:56.720 position:50% align:middle Sixteen percent of the LPN workforce reported holding an associate degree or higher as their highest degree 00:13:56.720 --> 00:14:00.200 position:50% align:middle of education, which is an increase over the 2020 number. 00:14:00.200 --> 00:14:11.340 position:50% align:middle The COVID-19 pandemic had a notable impact on the LPN workforce employment. 00:14:11.340 --> 00:14:15.570 position:50% align:middle Seventy-one percent of LPN licensees were working full-time. 00:14:15.570 --> 00:14:20.482 position:50% align:middle That's an increase, up from 66% in 2020. 00:14:20.482 --> 00:14:27.310 position:50% align:middle Post-pandemic inflation is reflected in LPN salaries. 00:14:27.310 --> 00:14:34.550 position:50% align:middle Median pre-tax earnings rose from $44,000 in 2020 to $50,000 in 2022. 00:14:34.550 --> 00:14:43.040 position:50% align:middle Over 55% of the LPN workforce reported being engaged in telehealth activities. 00:14:43.040 --> 00:14:50.880 position:50% align:middle The proportion actually is an increase over the numbers reported in previous years. 00:14:50.880 --> 00:14:57.440 position:50% align:middle Of those LPNs providing nursing services remotely, usage of video calls nearly tripled from 2020 to 2022. 00:14:57.440 --> 00:15:05.290 position:50% align:middle The use of electronic messaging also increased. 00:15:05.290 --> 00:15:11.350 position:50% align:middle Among LPNs who hold a multi-state license, three-quarters have not used it, and once again, 00:15:11.350 --> 00:15:16.192 position:50% align:middle about one-quarter have. 00:15:16.192 --> 00:15:27.240 position:50% align:middle And then, as with the RN survey, we asked questions about the impact of COVID 00:15:27.240 --> 00:15:29.350 position:50% align:middle on the LPN workforce. 00:15:29.350 --> 00:15:37.270 position:50% align:middle And 63% of the LPNs reported that their workload increased, 11% reported that they changed their 00:15:37.270 --> 00:15:45.840 position:50% align:middle practice setting, 10% reported they retired or left nursing, and 45% reported that they felt burnt 00:15:45.840 --> 00:15:52.860 position:50% align:middle out at least a few times a week as a result of the pandemic. 00:15:52.860 --> 00:16:01.150 position:50% align:middle To finish up, we will take a quick look at what the future may portend. 00:16:01.150 --> 00:16:08.650 position:50% align:middle In the survey, we do ask the question to nurses, do they plan to retire or leave nursing in the 00:16:08.650 --> 00:16:10.600 position:50% align:middle next five years? 00:16:10.600 --> 00:16:18.140 position:50% align:middle And so, in response to that question, what we found out is that a projected 800,000 RNs and 00:16:18.140 --> 00:16:23.830 position:50% align:middle 184,000 LPNs indicated that, yes, they are likely to leave nursing by 2027. 00:16:23.830 --> 00:16:32.790 position:50% align:middle This is equivalent to roughly 20% of the total licensed RN and LPN workforces in the U.S. 00:16:32.790 --> 00:16:37.910 position:50% align:middle And the obvious question is, while we always have attrition, you know, 00:16:37.910 --> 00:16:40.260 position:50% align:middle throughout a year, what would it typically be? 00:16:40.260 --> 00:16:43.940 position:50% align:middle And so we did look at, like, what it would have been before the pandemic. 00:16:43.940 --> 00:16:47.590 position:50% align:middle And in that time, we typically would have expected about half that number, 00:16:47.590 --> 00:16:52.430 position:50% align:middle and I think the rough estimate we came up with was 375,000 RNs. 00:16:52.430 --> 00:17:00.810 position:50% align:middle So just as a contrast, you know, like, this is a big deal. 00:17:00.810 --> 00:17:10.700 position:50% align:middle And what's really also of concern is that, of those people who said they plan on retiring 00:17:10.700 --> 00:17:21.130 position:50% align:middle or leaving, 24% of the RN total was in the younger career nurses range. 00:17:21.130 --> 00:17:28.110 position:50% align:middle So it's also of a concern that a good proportion of the ones who say they're going to leave are younger. 00:17:28.110 --> 00:17:36.850 position:50% align:middle So, in summary, in the wake of the COVID-19 pandemic, the nursing workforce has undergone a dramatic shift 00:17:36.850 --> 00:17:41.670 position:50% align:middle with the loss of hundreds of thousands of experienced RNs and LPNs. 00:17:41.670 --> 00:17:47.730 position:50% align:middle The workforce today is distinctly younger, more educated, and slightly more diverse. 00:17:47.730 --> 00:17:51.840 position:50% align:middle About half the RNs and LPNs engage in telehealth. 00:17:51.840 --> 00:17:55.590 position:50% align:middle Twenty percent of the total workforce may leave nursing by 2027. 00:17:55.590 --> 00:18:03.590 position:50% align:middle And I will mention, the results of the survey were published in a supplement to the April "Journal 00:18:03.590 --> 00:18:05.830 position:50% align:middle of Nursing Regulation." 00:18:05.830 --> 00:18:12.030 position:50% align:middle And as we indicated in our method section, we couldn't have done any of this data without your 00:18:12.030 --> 00:18:15.060 position:50% align:middle support and help, and we thank you for that. 00:18:15.060 --> 00:18:20.520 position:50% align:middle And going forward, we will, once again, be conducting a survey in 2024, 00:18:20.520 --> 00:18:23.930 position:50% align:middle and we are looking for your help again. 00:18:23.930 --> 00:18:34.800 position:50% align:middle And we are especially asking the executive officers to come over to our booth and consider giving us their 00:18:34.800 --> 00:18:40.130 position:50% align:middle consent so that we can use the Nursys data and proceed forward with the next survey. 00:18:40.130 --> 00:18:42.520 position:50% align:middle Our booth is in the corner over there. 00:18:42.520 --> 00:18:43.620 position:50% align:middle We've got candy. 00:18:43.620 --> 00:18:45.050 position:50% align:middle We have Skittles. 00:18:45.050 --> 00:18:46.580 position:50% align:middle Our people are friendly. 00:18:46.580 --> 00:18:47.530 position:50% align:middle Brandon's friendly. 00:18:47.530 --> 00:18:48.940 position:50% align:middle I think he is. 00:18:48.940 --> 00:18:50.310 position:50% align:middle I try to be friendly. 00:18:50.310 --> 00:18:55.150 position:50% align:middle So, please, come over, visit, and sign your life away. 00:18:55.150 --> 00:18:57.140 position:50% align:middle You're just signing some data away. 00:18:57.140 --> 00:19:05.030 position:50% align:middle And otherwise, that's all I've got, and I guess I will take your questions. 00:19:05.030 --> 00:19:06.580 position:50% align:middle Okay, microphone three. 00:19:06.580 --> 00:19:07.410 position:50% align:middle I'm seeing number three. 00:19:07.410 --> 00:19:07.740 position:50% align:middle Hello. 00:19:07.740 --> 00:19:09.450 position:50% align:middle - [Carrie] Hi, my name is Carrie Oliveira. 00:19:09.450 --> 00:19:11.360 position:50% align:middle I'm from the state of Hawaii. 00:19:11.360 --> 00:19:14.790 position:50% align:middle I'm also a member of the forum's research committee. 00:19:14.790 --> 00:19:15.130 position:50% align:middle - Yes. 00:19:15.130 --> 00:19:18.680 position:50% align:middle - My question about your exits for the younger portion of the workforce. 00:19:18.680 --> 00:19:22.320 position:50% align:middle Do you know that that's attributable to stress related to work? 00:19:22.320 --> 00:19:24.520 position:50% align:middle Is it COVID, or is it some other reason for the departures? 00:19:24.520 --> 00:19:27.210 position:50% align:middle Did you crosstab that? 00:19:27.210 --> 00:19:31.930 position:50% align:middle - Well, our researcher, Charlie O'Hara, one of our researchers, 00:19:31.930 --> 00:19:33.970 position:50% align:middle really has been digging into that. 00:19:33.970 --> 00:19:37.900 position:50% align:middle And certainly, the stress is one of the issues that he found. 00:19:37.900 --> 00:19:42.640 position:50% align:middle That's a big issue that it's related to. 00:19:42.640 --> 00:19:48.020 position:50% align:middle That is one of the reasons they're considering leaving. 00:19:48.020 --> 00:19:49.480 position:50% align:middle So, yes. 00:19:49.480 --> 00:19:52.810 position:50% align:middle The answer is yes, you're right on. 00:19:52.810 --> 00:19:54.180 position:50% align:middle Microphone six. 00:19:54.180 --> 00:19:55.220 position:50% align:middle - [Lynn] Thank you. 00:19:55.220 --> 00:19:56.820 position:50% align:middle Lynn Power, Canada. 00:19:56.820 --> 00:19:59.780 position:50% align:middle And I'm going to build on that same type of question. 00:19:59.780 --> 00:20:04.390 position:50% align:middle I know this is a national survey, and it's anonymous, and we're looking at larger numbers. 00:20:04.390 --> 00:20:11.280 position:50% align:middle But that intent to leave is like a big bubble that multiple groups are really scared of. 00:20:11.280 --> 00:20:18.380 position:50% align:middle Is there any way, are you planning any type of qualitative work to dig deeper to really maybe track 00:20:18.380 --> 00:20:20.860 position:50% align:middle individuals and see if they do leave? 00:20:20.860 --> 00:20:23.120 position:50% align:middle Is there more plans? 00:20:23.120 --> 00:20:26.640 position:50% align:middle Because right now, there's an awful lot of fatigue. 00:20:26.640 --> 00:20:32.140 position:50% align:middle So there's a lot of leaving versus will they really leave? 00:20:32.140 --> 00:20:35.940 position:50% align:middle And so, to do proper workforce modeling, we're kind of in a dicey spot. 00:20:35.940 --> 00:20:41.040 position:50% align:middle - Well, I will say, in our methods, it's a survey. 00:20:41.040 --> 00:20:48.870 position:50% align:middle And by IRB protocol, we deliberately do not know who answered to us. 00:20:48.870 --> 00:20:52.550 position:50% align:middle So, at any given time, we can't...it's not a cohort study. 00:20:52.550 --> 00:20:56.280 position:50% align:middle We can't individually track nurses who said they plan to leave. 00:20:56.280 --> 00:21:03.370 position:50% align:middle All we can do is check overall numbers and see, you know, how does this compare to what 00:21:03.370 --> 00:21:05.220 position:50% align:middle we were projecting? 00:21:05.220 --> 00:21:15.560 position:50% align:middle I know that we also ask the question in 2020, and there was a figure given there. 00:21:15.560 --> 00:21:24.160 position:50% align:middle And actually, if we came up with numbers there that, at that time, were projecting how many intend to leave 00:21:24.160 --> 00:21:30.360 position:50% align:middle compared to how many actually left, that actually undershot it. 00:21:30.360 --> 00:21:33.660 position:50% align:middle Like, in other words, if we tried to do this projection in 2020, 00:21:33.660 --> 00:21:41.090 position:50% align:middle we would have undershot the actual amount that left. 00:21:41.090 --> 00:21:42.100 position:50% align:middle And, Brendan. 00:21:42.100 --> 00:21:42.330 position:50% align:middle - [Brendan] Yeah. 00:21:42.330 --> 00:21:43.260 position:50% align:middle And I would just add. 00:21:43.260 --> 00:21:44.650 position:50% align:middle So, I mean, Richard is exactly right. 00:21:44.650 --> 00:21:50.790 position:50% align:middle So we added in 2020 for the first time ever that intent to leave question, and so we do track what is the 00:21:50.790 --> 00:21:53.680 position:50% align:middle attrition in between, like, our survey cycle years. 00:21:53.680 --> 00:21:58.300 position:50% align:middle And what we would have anticipated, obviously, two years out is about 40% of the way to that number. 00:21:58.300 --> 00:22:00.640 position:50% align:middle To Richard's exact point, we've seen more. 00:22:00.640 --> 00:22:04.000 position:50% align:middle We attribute that largely to the pandemic, which obviously was not on the radar in 2020, 00:22:04.000 --> 00:22:08.480 position:50% align:middle but we have eclipsed that, where we would be in terms of the pace of that. 00:22:08.480 --> 00:22:12.740 position:50% align:middle The other thing I just wanted to mention is, in our COVID question set, which, you know, 00:22:12.740 --> 00:22:16.490 position:50% align:middle we have limited time today for this presentation, which is why we would encourage you to download 00:22:16.490 --> 00:22:22.590 position:50% align:middle the results, we did ask, actually, a qualitative free text question where we said, 00:22:22.590 --> 00:22:27.360 position:50% align:middle you know, "Using your own voice, your organic rationale, tell us, like, 00:22:27.360 --> 00:22:30.120 position:50% align:middle behind the scenes, what is it that was ultimately very, very problematic." 00:22:30.120 --> 00:22:36.410 position:50% align:middle And that's where Charlie O'Hara really came in and was able to basically apply supervised machine learning, 00:22:36.410 --> 00:22:40.500 position:50% align:middle natural language processing techniques to really delineate the objective trends 00:22:40.500 --> 00:22:42.240 position:50% align:middle from those qualitative responses. 00:22:42.240 --> 00:22:46.780 position:50% align:middle And to your point, we were able to distill some more tangible things that, essentially, 00:22:46.780 --> 00:22:51.960 position:50% align:middle the pandemic was accelerating or exacerbating but not necessarily was the primary cause. 00:22:51.960 --> 00:22:55.170 position:50% align:middle So it was things like safe staffing, you know, salary, etc. 00:22:55.170 --> 00:23:03.630 position:50% align:middle - And those results actually were also published not in the supplement but in the actual April JNR issue. 00:23:03.630 --> 00:23:10.270 position:50% align:middle I think it's the lead article in the April JNR issue, and it's beyond the firewall. 00:23:10.270 --> 00:23:12.770 position:50% align:middle So anybody can just download it because it's COVID. 00:23:12.770 --> 00:23:17.630 position:50% align:middle So Elsevier just said, "Yes, you know, we will put this out there to the public." 00:23:17.630 --> 00:23:18.600 position:50% align:middle So thank you. 00:23:18.600 --> 00:23:20.700 position:50% align:middle And I'm getting number seven. 00:23:20.700 --> 00:23:21.890 position:50% align:middle Everybody's telling me number seven. 00:23:21.890 --> 00:23:22.650 position:50% align:middle - [Hank] Good morning. 00:23:22.650 --> 00:23:26.000 position:50% align:middle Hank Chaudhry, CEO of the Federation of State Medical Boards. 00:23:26.000 --> 00:23:30.350 position:50% align:middle Thank you very much for sharing your research and your findings. 00:23:30.350 --> 00:23:35.890 position:50% align:middle I find it fascinating and more than a little worrying, of course, as well, in terms of the workforce. 00:23:35.890 --> 00:23:43.360 position:50% align:middle In medicine, we too saw a number of physicians and physician assistants who left practice as a result 00:23:43.360 --> 00:23:46.370 position:50% align:middle of COVID, not as dramatic as in nursing. 00:23:46.370 --> 00:23:52.290 position:50% align:middle But we also saw a number of state medical boards telling us that a lot of those physicians and PAs are 00:23:52.290 --> 00:23:58.370 position:50% align:middle returning to practice, so much so that my organization, the Federation of State Medical Boards, 00:23:58.370 --> 00:24:02.660 position:50% align:middle has put together a workgroup to look at the issue of physician reentry. 00:24:02.660 --> 00:24:07.970 position:50% align:middle In other words, if you've been gone a certain number of time, what criteria should state boards use to enable 00:24:07.970 --> 00:24:09.280 position:50% align:middle them to come back into practice? 00:24:09.280 --> 00:24:10.550 position:50% align:middle Do they need an assessment? 00:24:10.550 --> 00:24:14.820 position:50% align:middle Do they need some other means of demonstrating that they've kept up with the medicine? 00:24:14.820 --> 00:24:21.400 position:50% align:middle Have you seen something similar happening in nursing yet where nurses want to come back? 00:24:21.400 --> 00:24:26.460 position:50% align:middle - We haven't specifically seen that, but on our research agenda, 00:24:26.460 --> 00:24:33.980 position:50% align:middle we're going to be doing...we're planning going forward a lot more research out of this database. 00:24:33.980 --> 00:24:41.320 position:50% align:middle And one of the projects we will be studying are, specifically, as we said, 00:24:41.320 --> 00:24:47.990 position:50% align:middle 90% of the workforce is working in nursing. 00:24:47.990 --> 00:24:51.250 position:50% align:middle And so we do have questions about, okay, what about the other 10%? 00:24:51.250 --> 00:24:52.980 position:50% align:middle What are the prospects there? 00:24:52.980 --> 00:24:58.350 position:50% align:middle But as for your specific thing about reentry, we haven't specifically been asking 00:24:58.350 --> 00:24:59.710 position:50% align:middle questions about that. 00:24:59.710 --> 00:25:06.700 position:50% align:middle I mean, I think that could be an interesting study topic in terms of an approach to it. 00:25:06.700 --> 00:25:10.210 position:50% align:middle But we don't have data on that at the moment. 00:25:10.210 --> 00:25:15.570 position:50% align:middle - I was furiously trying to press the push button on my microphone up here too. 00:25:15.570 --> 00:25:17.170 position:50% align:middle So I was just going to follow up to that. 00:25:17.170 --> 00:25:22.300 position:50% align:middle You know, that's one of the reasons behind the logic of going to field every two years rather than every four, 00:25:22.300 --> 00:25:24.650 position:50% align:middle which was historically done by HRSA. 00:25:24.650 --> 00:25:28.070 position:50% align:middle We really feel as though, in particular, with flash points and inflection points, 00:25:28.070 --> 00:25:30.000 position:50% align:middle like the pandemic, it's going to change rapidly. 00:25:30.000 --> 00:25:33.560 position:50% align:middle So that's one of the reasons why we'll be back in field for 2024. 00:25:33.560 --> 00:25:36.690 position:50% align:middle So we'll do that plug again for the executive officers. 00:25:36.690 --> 00:25:40.910 position:50% align:middle But it is one of those issues where we're trying to understand, you know, 00:25:40.910 --> 00:25:43.340 position:50% align:middle really what are all the inputs on this. 00:25:43.340 --> 00:25:48.030 position:50% align:middle And so it's one of the reasons why, in April, we really tried to heighten the publication, like, 00:25:48.030 --> 00:25:52.700 position:50% align:middle the results of this study, to bring together inter-professional stakeholders 00:25:52.700 --> 00:25:56.370 position:50% align:middle to really kind of have an all-hands-on-deck approach to policy solutions. 00:25:56.370 --> 00:26:01.275 position:50% align:middle Because I think what you're speaking to there is there could be a natural kind of boomerang effect. 00:26:01.275 --> 00:26:04.390 position:50% align:middle There could be other instances in which, through intentional policy, 00:26:04.390 --> 00:26:06.080 position:50% align:middle we could encourage folks to come back too. 00:26:06.080 --> 00:26:07.060 position:50% align:middle - And thank you. 00:26:07.060 --> 00:26:12.350 position:50% align:middle I'll just mention briefly that two of the drivers we're seeing in medicine for physicians and PAs wanting 00:26:12.350 --> 00:26:16.520 position:50% align:middle to come back, one is, of course, the pandemic has largely gone away, 00:26:16.520 --> 00:26:18.080 position:50% align:middle even though COVID is still around. 00:26:18.080 --> 00:26:23.880 position:50% align:middle But the other is the economy and a desire to sort of, "Let me go back into practice and see if I can 00:26:23.880 --> 00:26:24.580 position:50% align:middle earn some money." 00:26:24.580 --> 00:26:25.310 position:50% align:middle Thank you. 00:26:25.310 --> 00:26:28.200 position:50% align:middle - Thank you very much. 00:26:28.200 --> 00:26:29.140 position:50% align:middle And number two. 00:26:29.140 --> 00:26:31.940 position:50% align:middle - [Jacqueline] Jacqueline Wilmot, from Virginia. 00:26:31.940 --> 00:26:35.050 position:50% align:middle From the data, are you able to tell the shelf life of a nurse? 00:26:35.050 --> 00:26:42.830 position:50% align:middle In other words, how long from licensure to departure are they staying? 00:26:42.830 --> 00:26:46.170 position:50% align:middle - We ask questions. 00:26:46.170 --> 00:26:51.910 position:50% align:middle I mean, we actually, in raw form, do have that because we know how long nurses have 00:26:51.910 --> 00:26:54.920 position:50% align:middle been licensed, and we can see that over time. 00:26:54.920 --> 00:26:57.850 position:50% align:middle And we know that's... 00:26:57.850 --> 00:27:05.510 position:50% align:middle I was giving median age, but one of the other things we could look at is just 00:27:05.510 --> 00:27:12.380 position:50% align:middle studies of numbers of years licensed, which we do have and which also does change over time. 00:27:12.380 --> 00:27:13.400 position:50% align:middle That can be studied. 00:27:13.400 --> 00:27:16.340 position:50% align:middle I mean, we haven't focused on that, but we can tell. 00:27:16.340 --> 00:27:19.030 position:50% align:middle I think we have an idea of what that is. 00:27:19.030 --> 00:27:19.690 position:50% align:middle - Yeah. 00:27:19.690 --> 00:27:24.570 position:50% align:middle And you know, I would just reiterate what Richard said at kind of the front of the presentation in that the 00:27:24.570 --> 00:27:29.310 position:50% align:middle core of our surveys really constituted the minimum data set, and then layered on top of that are custom 00:27:29.310 --> 00:27:33.450 position:50% align:middle elements regarding, like, specialized topics that we want to track over time. 00:27:33.450 --> 00:27:36.840 position:50% align:middle That is not one of the specific variables that we've ever dug into. 00:27:36.840 --> 00:27:41.190 position:50% align:middle But to Richard's point, because so many jurisdictions are Nursys-participating, 00:27:41.190 --> 00:27:44.690 position:50% align:middle we are able to look at, for instance, number of years licensed. 00:27:44.690 --> 00:27:49.360 position:50% align:middle Now, I will say that, typically, even in retirement, nurses do not give up their licenses. 00:27:49.360 --> 00:27:55.040 position:50% align:middle So there is some caution in terms of interpreting that data point because it necessarily isn't a full 00:27:55.040 --> 00:27:57.280 position:50% align:middle one-to-one with years in practice. 00:27:57.280 --> 00:27:58.800 position:50% align:middle But it's a great question. 00:27:58.800 --> 00:28:00.000 position:50% align:middle - Thank you. 00:28:00.000 --> 00:28:01.520 position:50% align:middle - And microphone number eight. 00:28:01.520 --> 00:28:03.380 position:50% align:middle - [Jennifer] Hi. 00:28:03.380 --> 00:28:05.110 position:50% align:middle Jennifer Manning, Louisiana. 00:28:05.110 --> 00:28:12.080 position:50% align:middle My question pertains to the intent to leave and, specifically, even those who have left. 00:28:12.080 --> 00:28:17.420 position:50% align:middle Is there a way to capture those who have gone back to get advanced education? 00:28:17.420 --> 00:28:23.710 position:50% align:middle I'm specifically thinking about how many more nurse practitioners we have trained in the last few years. 00:28:23.710 --> 00:28:27.820 position:50% align:middle Are they counted as nurses who left, or is there a way to tease out that they have gone 00:28:27.820 --> 00:28:35.470 position:50% align:middle on to get advanced education? 00:28:35.470 --> 00:28:43.730 position:50% align:middle - Actually, if I'm understanding your question, if they go to education, we track that. 00:28:43.730 --> 00:28:45.120 position:50% align:middle We know how many that is. 00:28:45.120 --> 00:28:52.460 position:50% align:middle They would not be considered, like...that's not considered full-time. 00:28:52.460 --> 00:28:55.370 position:50% align:middle Like, we're asking, are people working part-time or full-time? 00:28:55.370 --> 00:29:03.330 position:50% align:middle And then a follow-up question we ask, and this is, you know, as part of the data, just directly, like, 00:29:03.330 --> 00:29:06.690 position:50% align:middle "Okay, you know, why are you not working?" 00:29:06.690 --> 00:29:10.510 position:50% align:middle And education is one of the reasons there, and we do track that. 00:29:10.510 --> 00:29:15.160 position:50% align:middle Like, if they're just going back for education, we can identify the numbers in that and 00:29:15.160 --> 00:29:15.910 position:50% align:middle break that down. 00:29:15.910 --> 00:29:16.960 position:50% align:middle - Yeah. 00:29:16.960 --> 00:29:19.060 position:50% align:middle I would just completely echo Richard's comments. 00:29:19.060 --> 00:29:22.370 position:50% align:middle So we are able to delineate between, you know, Lynn had the question, 00:29:22.370 --> 00:29:26.080 position:50% align:middle there are other questions regarding the motivation of folks, in particular, the younger cohort, 00:29:26.080 --> 00:29:27.750 position:50% align:middle in terms of their intent to leave. 00:29:27.750 --> 00:29:33.680 position:50% align:middle What we were very careful to do is to delineate between something like the pandemic causing burnout and stress 00:29:33.680 --> 00:29:37.780 position:50% align:middle and something like just kind of a natural transition where one individual might want to return to school 00:29:37.780 --> 00:29:39.010 position:50% align:middle to advance their degree. 00:29:39.010 --> 00:29:41.080 position:50% align:middle So we are able to tease that out in the data, and we did. 00:29:41.080 --> 00:29:48.230 position:50% align:middle And so what Richard focused on I think really well is that when we say a quarter of that 800,000 RNs intend 00:29:48.230 --> 00:29:50.760 position:50% align:middle to leave, that is specifically due to the pandemic. 00:29:50.760 --> 00:29:56.010 position:50% align:middle So these folks are essentially, at least point in time, telling us that they have no intention to return. 00:29:56.010 --> 00:30:00.210 position:50% align:middle Now, to Hank's point, you know, whether or not that that remains firm, right, 00:30:00.210 --> 00:30:01.550 position:50% align:middle this is an intent to leave. 00:30:01.550 --> 00:30:04.930 position:50% align:middle This is a self-report data point at a particular point in time. 00:30:04.930 --> 00:30:10.560 position:50% align:middle There are many, many factors that come to bear on that decision long-term, but at least right now, 00:30:10.560 --> 00:30:13.520 position:50% align:middle it's about a quarter saying, you know, in that kind of younger than 36, 00:30:13.520 --> 00:30:15.610 position:50% align:middle fewer than 10 years' work experience. 00:30:15.610 --> 00:30:18.780 position:50% align:middle And that's what was really causing significant concern when we reviewed the results. 00:30:18.780 --> 00:30:20.040 position:50% align:middle - Thank you. 00:30:20.040 --> 00:30:20.900 position:50% align:middle - Okay. 00:30:20.900 --> 00:30:22.710 position:50% align:middle And I'll go to microphone seven. 00:30:22.710 --> 00:30:24.690 position:50% align:middle - [Peggy] Hi, Richard. 00:30:24.690 --> 00:30:26.530 position:50% align:middle This is Peggy Benson, from Alabama. 00:30:26.530 --> 00:30:31.440 position:50% align:middle We ran our demographics study too, and we saw, in the next 5 years, 00:30:31.440 --> 00:30:35.970 position:50% align:middle we had 38,000 who had an intent to retire. 00:30:35.970 --> 00:30:39.450 position:50% align:middle So we looked at the supply and demand, and then we started looking at, well, 00:30:39.450 --> 00:30:44.940 position:50% align:middle how many exams do we get every year, how many endorsements that come into Alabama. 00:30:44.940 --> 00:30:49.150 position:50% align:middle And we saw, in the 5-year period, that 38,000 were going to leave. 00:30:49.150 --> 00:30:55.800 position:50% align:middle We were going to get 37,000 back in, but they're going to be a new workforce. 00:30:55.800 --> 00:31:03.860 position:50% align:middle So we've started concentrating our efforts on working with the employers to get a mentoring system so that 00:31:03.860 --> 00:31:06.760 position:50% align:middle they can pull the older nurses back in. 00:31:06.760 --> 00:31:13.600 position:50% align:middle So, with that said, are you guys looking at, if we're going to lose a million in next five, 00:31:13.600 --> 00:31:15.900 position:50% align:middle how many are coming in, in the next five? 00:31:15.900 --> 00:31:22.280 position:50% align:middle And it looks like we need to have a strategy for all of these new people who are going to lack that mentorship. 00:31:22.280 --> 00:31:25.120 position:50% align:middle - Yeah. 00:31:25.120 --> 00:31:32.880 position:50% align:middle I mean, to the first point, like, if you're losing 38,000 and you're gaining 37,000, 00:31:32.880 --> 00:31:37.750 position:50% align:middle that's okay, unless your need is increasing. 00:31:37.750 --> 00:31:42.250 position:50% align:middle And if your need, you know, if the demand is increasing, then actually, 00:31:42.250 --> 00:31:44.140 position:50% align:middle even that is not sufficient. 00:31:44.140 --> 00:31:51.630 position:50% align:middle But assume the demand is static and you're okay there, I applaud you for doing what you're doing, 00:31:51.630 --> 00:31:56.120 position:50% align:middle which is looking at that younger group and saying, "What can we do to maintain them?" 00:31:56.120 --> 00:31:59.480 position:50% align:middle Because to look at the future here, it doesn't have to be. 00:31:59.480 --> 00:32:00.280 position:50% align:middle Like, we're just saying... 00:32:00.280 --> 00:32:07.470 position:50% align:middle They're saying, they anticipate that they're going to leave, but circumstances can change. 00:32:07.470 --> 00:32:15.650 position:50% align:middle Our research is showing this could be stress, this could be burnout, this could be environment. 00:32:15.650 --> 00:32:21.710 position:50% align:middle It could be things that possibly could be changed so that that doesn't have to come to fruition. 00:32:21.710 --> 00:32:24.620 position:50% align:middle So that's the right focus there. 00:32:24.620 --> 00:32:28.940 position:50% align:middle - You know, I was just going to say, I too applaud you. 00:32:28.940 --> 00:32:33.200 position:50% align:middle I think you're doing exactly the right thing, and I think you are touching upon something that we 00:32:33.200 --> 00:32:35.940 position:50% align:middle thought about constantly when we were reviewing these results. 00:32:35.940 --> 00:32:40.080 position:50% align:middle It's not just the fact that we have this intent to leave, but even the folks coming into the 00:32:40.080 --> 00:32:44.400 position:50% align:middle workforce now, because of that generational shift that Richard was able to document, 00:32:44.400 --> 00:32:50.050 position:50% align:middle there are fewer and fewer mentors with significant practice experience in many of these settings. 00:32:50.050 --> 00:32:55.450 position:50% align:middle And those who would theoretically step into those walls we are seeing burned with high levels 00:32:55.450 --> 00:32:56.890 position:50% align:middle of stress and burnout. 00:32:56.890 --> 00:33:00.480 position:50% align:middle So it's really kind of, you know, battered from all sides, so to speak. 00:33:00.480 --> 00:33:04.500 position:50% align:middle And so when we see these data points, we try to think about it kind of in the constellation 00:33:04.500 --> 00:33:05.680 position:50% align:middle of everything else going on. 00:33:05.680 --> 00:33:08.680 position:50% align:middle So, yes, you know, I just wanted to stand up and say, we applaud Alabama too. 00:33:08.680 --> 00:33:12.250 position:50% align:middle I think it really is critical that you think about all these things in concert. 00:33:12.250 --> 00:33:17.090 position:50% align:middle The one thing that I would, like, refer back to that Richard I think nicely stated, 00:33:17.090 --> 00:33:25.400 position:50% align:middle is that when we say 800,000 in the next 5 years, we did do an analysis looking at exam passes, 00:33:25.400 --> 00:33:30.130 position:50% align:middle we did do an analysis looking at annual retirements, you know, maybe somebody transitions out into, like, 00:33:30.130 --> 00:33:32.350 position:50% align:middle a graduate nursing program, etc. 00:33:32.350 --> 00:33:37.710 position:50% align:middle And that's where Richard noted that what we would anticipate when we take into account the inflow and the 00:33:37.710 --> 00:33:42.550 position:50% align:middle potential outflow on an annual basis, what we would anticipate is about 375,000 nurses 00:33:42.550 --> 00:33:47.040 position:50% align:middle leaving in the next 5 years just due to natural progression. 00:33:47.040 --> 00:33:51.830 position:50% align:middle That 800,000 number, doubling that, is obviously deeply concerning because, 00:33:51.830 --> 00:33:56.350 position:50% align:middle whether or not we can account for that and backfill that with just, you know, new entrance, 00:33:56.350 --> 00:33:59.460 position:50% align:middle with all the other challenges going on, shortages in nursing faculty. 00:33:59.460 --> 00:34:04.860 position:50% align:middle You know, one of the things that's going to be in my presentation, AACN has reported drop-off in enrollment 00:34:04.860 --> 00:34:10.290 position:50% align:middle to baccalaureate programs, even applications, so prospective student interest in nursing programs. 00:34:10.290 --> 00:34:11.850 position:50% align:middle It's kind of at both ends of the funnel. 00:34:11.850 --> 00:34:13.570 position:50% align:middle - Yeah. 00:34:13.570 --> 00:34:14.750 position:50% align:middle - Thank you. 00:34:14.750 --> 00:34:16.590 position:50% align:middle - And number eight. 00:34:16.590 --> 00:34:17.920 position:50% align:middle - [Phyllis] Hi. 00:34:17.920 --> 00:34:20.450 position:50% align:middle Phyllis Johnson, EO, Mississippi. 00:34:20.450 --> 00:34:21.970 position:50% align:middle Great presentation. 00:34:21.970 --> 00:34:23.380 position:50% align:middle Very disturbing results. 00:34:23.380 --> 00:34:26.910 position:50% align:middle I've reviewed the study even prior to this. 00:34:26.910 --> 00:34:32.150 position:50% align:middle I guess my question is, we've seen a resurgence of LPNs being utilized and 00:34:32.150 --> 00:34:38.160 position:50% align:middle brought back into the hospital setting, and in your study, did you look at nurses...are these 00:34:38.160 --> 00:34:42.600 position:50% align:middle nurses leaving the bedside because there's still a great need for that? 00:34:42.600 --> 00:34:47.090 position:50% align:middle And also, why are they leaving the bedside even though they are leaving the profession? 00:34:47.090 --> 00:34:51.820 position:50% align:middle Some of them are not leaving the profession, but they're going into their own private business 00:34:51.820 --> 00:34:56.670 position:50% align:middle with other opportunities for nursing, such as aesthetics and things of that nature. 00:34:56.670 --> 00:35:04.170 position:50% align:middle Will there be any opportunity in your future research to look at where the nurses are that have 00:35:04.170 --> 00:35:06.020 position:50% align:middle left the profession? 00:35:06.020 --> 00:35:12.660 position:50% align:middle Because that's what we're seeing a lot in our region, especially, in Mississippi, I notice that a lot. 00:35:12.660 --> 00:35:13.450 position:50% align:middle So thank you. 00:35:13.450 --> 00:35:14.170 position:50% align:middle - Yeah. 00:35:14.170 --> 00:35:19.620 position:50% align:middle I think that is one of our future studies. 00:35:19.620 --> 00:35:20.810 position:50% align:middle Sort of exactly that. 00:35:20.810 --> 00:35:24.270 position:50% align:middle - You know, I'll just refer back to that all-hands-on-deck approach. 00:35:24.270 --> 00:35:29.530 position:50% align:middle Phyllis, again, like spot on observation, one of the targeted sub-analyses that we have planned 00:35:29.530 --> 00:35:33.550 position:50% align:middle for this cycle, really kind of taking us through the end of the calendar year 2023, 00:35:33.550 --> 00:35:39.460 position:50% align:middle is to look at the nurses who are actively employed but in non-nursing positions, 00:35:39.460 --> 00:35:41.570 position:50% align:middle kind of representing the lowest hanging fruit, right? 00:35:41.570 --> 00:35:45.500 position:50% align:middle These folks are vetted, we know their skills, we know their competency, right, 00:35:45.500 --> 00:35:47.070 position:50% align:middle they're already licensed, etc. 00:35:47.070 --> 00:35:49.260 position:50% align:middle How can we bring them off of the sidelines? 00:35:49.260 --> 00:35:52.230 position:50% align:middle And then, from there, kind of start to layer on top of that conversation 00:35:52.230 --> 00:35:54.330 position:50% align:middle for increasing enrollment, etc., etc. 00:35:54.330 --> 00:35:59.070 position:50% align:middle But I think the first step, and I think Hank actually spoke to it a little bit 00:35:59.070 --> 00:36:05.450 position:50% align:middle with other health professions, is, how do we get, essentially, as close to as humanly possible full 00:36:05.450 --> 00:36:08.080 position:50% align:middle employment in nursing of qualified nurses? 00:36:08.080 --> 00:36:09.240 position:50% align:middle - Yeah. 00:36:09.240 --> 00:36:10.130 position:50% align:middle Okay, thank you. 00:36:10.130 --> 00:36:11.650 position:50% align:middle And then microphone two. 00:36:11.650 --> 00:36:12.110 position:50% align:middle - [Silvie] Hi. 00:36:12.110 --> 00:36:13.880 position:50% align:middle Silvie Crawford, Ontario, Canada. 00:36:13.880 --> 00:36:19.000 position:50% align:middle So just building on the opportunities here if they don't have enough research, 00:36:19.000 --> 00:36:22.740 position:50% align:middle but I think there would also be an interest in considering, with the shift, 00:36:22.740 --> 00:36:27.890 position:50% align:middle the novice to the seasoned and having more novice, it'd be interesting to know any impact to the public 00:36:27.890 --> 00:36:30.143 position:50% align:middle safety mandate, conduct issues. 00:36:30.143 --> 00:36:32.040 position:50% align:middle So it's kind of just really weaving that in. 00:36:32.040 --> 00:36:34.270 position:50% align:middle I think that would be an interesting area to explore. 00:36:34.270 --> 00:36:34.740 position:50% align:middle Thank you. 00:36:34.740 --> 00:36:35.800 position:50% align:middle - Okay. 00:36:35.800 --> 00:36:36.380 position:50% align:middle Thank you. 00:36:36.380 --> 00:36:40.480 position:50% align:middle And then microphone nine, which is...okay. 00:36:40.480 --> 00:36:41.370 position:50% align:middle - [Jim] Thank you. 00:36:41.370 --> 00:36:42.280 position:50% align:middle Jim Campbell, WHO. 00:36:42.280 --> 00:36:44.430 position:50% align:middle Thank you, Richard, Brendan. 00:36:44.430 --> 00:36:48.780 position:50% align:middle Great work here in terms of adding to the evidence space. 00:36:48.780 --> 00:36:50.680 position:50% align:middle Just a couple of remarks, if I may. 00:36:50.680 --> 00:36:59.050 position:50% align:middle On the intention to leave/retirement, it'd be really useful if we could sort of disaggregate 00:36:59.050 --> 00:37:06.270 position:50% align:middle between what is retirement against early departure from the service. 00:37:06.270 --> 00:37:14.730 position:50% align:middle We have, globally, the suggestion of a great resignation in the clinical workforce, 00:37:14.730 --> 00:37:20.130 position:50% align:middle which is largely fueled by some of the data coming out of the United States. 00:37:20.130 --> 00:37:28.570 position:50% align:middle But given that most of that is not on actual departure but intention to depart, 00:37:28.570 --> 00:37:34.860 position:50% align:middle we really have a bit of a sort of scientific responsibility to disaggregate some of those issues. 00:37:34.860 --> 00:37:41.850 position:50% align:middle One of the options is to look at things such as stability index, look at survival rates with that data 00:37:41.850 --> 00:37:45.800 position:50% align:middle to really try and nail down, disaggregate. 00:37:45.800 --> 00:37:50.340 position:50% align:middle So I'd welcome the opportunity to look at how we might be able to support some of that. 00:37:50.340 --> 00:37:54.050 position:50% align:middle The second point, which really, Richard, just if you could answer, 00:37:54.050 --> 00:38:05.960 position:50% align:middle what we see globally is that an increase in wages triggers an increase in employment of male nurses. 00:38:05.960 --> 00:38:13.980 position:50% align:middle And I wonder if your data is able to distinguish between, you know, is it the horse before the cart, 00:38:13.980 --> 00:38:15.540 position:50% align:middle the chicken and the egg? 00:38:15.540 --> 00:38:23.130 position:50% align:middle And is some of that increase in wages and increase in percentage male linked to additional hours worked 00:38:23.130 --> 00:38:29.450 position:50% align:middle during the COVID-19 pandemic, or is it an actual uplift in nurse wages for RNs? 00:38:29.450 --> 00:38:32.190 position:50% align:middle Because that benefits the entire workforce. 00:38:32.190 --> 00:38:35.610 position:50% align:middle So more men equals more money, which equals more money for women. 00:38:35.610 --> 00:38:39.670 position:50% align:middle I just wonder if you've got any better understanding about that. 00:38:39.670 --> 00:38:47.010 position:50% align:middle - I think that's a difficult one to untangle, especially with the nature of the survey data. 00:38:47.010 --> 00:38:52.990 position:50% align:middle A long time ago, I looked into some of the gender stuff. 00:38:52.990 --> 00:38:54.840 position:50% align:middle I'm sure. 00:38:54.840 --> 00:39:03.680 position:50% align:middle One of the breakdowns we do, anybody who knows this, like, anytime I've tried to break down wages 00:39:03.680 --> 00:39:15.500 position:50% align:middle by male/female, breaking it down by practice level, by setting, by geography, 00:39:15.500 --> 00:39:23.650 position:50% align:middle the gap never goes away between men and women, you know, there's all this, oh, 00:39:23.650 --> 00:39:28.190 position:50% align:middle this is a different specialty, or this wage gap is coming because men 00:39:28.190 --> 00:39:30.060 position:50% align:middle are working here. 00:39:30.060 --> 00:39:32.380 position:50% align:middle You always find something. 00:39:32.380 --> 00:39:36.420 position:50% align:middle And so, I mean, I don't know, specifically. 00:39:36.420 --> 00:39:41.080 position:50% align:middle I mean, we've been seeing an increase in number of men in the field, and I don't know. 00:39:41.080 --> 00:39:46.770 position:50% align:middle Is it because wages are going up? 00:39:46.770 --> 00:39:49.540 position:50% align:middle Chicken or the egg, like, in other words, what's driving what? 00:39:49.540 --> 00:39:59.260 position:50% align:middle I don't know if our survey has the capacity to actually make any distinction like that in terms 00:39:59.260 --> 00:40:01.500 position:50% align:middle of what's driving what. 00:40:01.500 --> 00:40:02.500 position:50% align:middle - Yeah. 00:40:02.500 --> 00:40:06.860 position:50% align:middle You know, I would just add too because, you know, Richard did have that in his presentation. 00:40:06.860 --> 00:40:11.030 position:50% align:middle We've seen such a steady, although, certainly not a spike, in any capacity, 00:40:11.030 --> 00:40:16.400 position:50% align:middle but such a steady increase in the number of male nurses in the United States that I think we should be very 00:40:16.400 --> 00:40:20.420 position:50% align:middle careful or conservative in over-interpreting the impact of the pandemic, specifically. 00:40:20.420 --> 00:40:25.070 position:50% align:middle This is part of a long-term trend from where we picked up the survey in 2013 to now. 00:40:25.070 --> 00:40:27.960 position:50% align:middle The other thing is, too, and I know you know this as well, 00:40:27.960 --> 00:40:31.410 position:50% align:middle salary is such a tricky thing, and particularly, United States, for nursing, 00:40:31.410 --> 00:40:35.480 position:50% align:middle because there was salary inflation with kind of the rise in travel nurses. 00:40:35.480 --> 00:40:39.490 position:50% align:middle There's also been significant inflation, which has, you know, kind of changed the game in terms of salary. 00:40:39.490 --> 00:40:44.370 position:50% align:middle So what it represents in terms of permanent salary increase, which I think would be intentional policy, 00:40:44.370 --> 00:40:49.850 position:50% align:middle what speaks to your comment, versus more kind of temporary spikes that are kind 00:40:49.850 --> 00:40:51.990 position:50% align:middle of driven or an artifact of the pandemic. 00:40:51.990 --> 00:40:56.150 position:50% align:middle I think that's to be determined, and I think that that's why, you know, again, 00:40:56.150 --> 00:40:57.830 position:50% align:middle we come back into the field every two years. 00:40:57.830 --> 00:41:02.730 position:50% align:middle Because some of these issues are so time-based and are so sensitive to, like, 00:41:02.730 --> 00:41:07.010 position:50% align:middle kind of the current circumstances in that two-year cycle that we've tried to kind of get a fresh look 00:41:07.010 --> 00:41:11.320 position:50% align:middle at it on a fairly regular basis. 00:41:11.320 --> 00:41:16.020 position:50% align:middle - Question eight. 00:41:16.020 --> 00:41:17.400 position:50% align:middle Yeah, microphone eight. 00:41:17.400 --> 00:41:21.470 position:50% align:middle - [Jose] I can be question eight. 00:41:21.470 --> 00:41:24.740 position:50% align:middle I'm Jose Castillo, from the great sunshine state of Florida. 00:41:24.740 --> 00:41:25.500 position:50% align:middle Woo-hoo. 00:41:25.500 --> 00:41:29.370 position:50% align:middle It's raining outside. 00:41:29.370 --> 00:41:32.160 position:50% align:middle I got two comments and a question. 00:41:32.160 --> 00:41:40.060 position:50% align:middle I do have a fraction of the data just based on the member services survey from the American Association 00:41:40.060 --> 00:41:46.640 position:50% align:middle of Nurse Anesthetists, and 51% are male and 49% are female based on the 00:41:46.640 --> 00:41:48.140 position:50% align:middle most recent survey. 00:41:48.140 --> 00:41:54.780 position:50% align:middle So I do think that that answers partly the question of the previous speaker or questioner/commenter. 00:41:54.780 --> 00:42:00.180 position:50% align:middle My second comment is on the Federation of Medical Board's comment from earlier. 00:42:00.180 --> 00:42:05.500 position:50% align:middle I do think that it is important to note that, as a regulatory body, 00:42:05.500 --> 00:42:12.640 position:50% align:middle we need to address the reentry component, especially if we don't know how many years they've been 00:42:12.640 --> 00:42:19.040 position:50% align:middle out and the competency and proficiency could be in question, especially in practice. 00:42:19.040 --> 00:42:25.000 position:50% align:middle The third item that I have a question with is with the small increase in the diverse practitioners component 00:42:25.000 --> 00:42:26.180 position:50% align:middle of the survey. 00:42:26.180 --> 00:42:32.650 position:50% align:middle I am looking through the lens, again, of diversity, and I know that I brought this up at mid-year. 00:42:32.650 --> 00:42:42.080 position:50% align:middle With English as a second language for our NCLEX-RN entry, and this could be one of the solutions for us 00:42:42.080 --> 00:42:47.220 position:50% align:middle to increase the workforce because we know that, with English, there's backward translation, 00:42:47.220 --> 00:42:50.850 position:50% align:middle forward translation, sideways translation, you name it. 00:42:50.850 --> 00:42:56.530 position:50% align:middle So I'm wondering if the study on item, and I have to read this, 00:42:56.530 --> 00:43:06.740 position:50% align:middle item function differential for a non-native English speaker versus an English speaker would be done. 00:43:06.740 --> 00:43:13.620 position:50% align:middle I believe that Phil addressed it last mid-year, but I just want to put it at the forefront if we can 00:43:13.620 --> 00:43:22.140 position:50% align:middle push that agenda so that we can get that data and probably truly know the source of why we have a huge 00:43:22.140 --> 00:43:28.200 position:50% align:middle non-pass rate for our ESL soon-to-be practitioners. 00:43:28.200 --> 00:43:28.560 position:50% align:middle Thank you. 00:43:28.560 --> 00:43:30.420 position:50% align:middle - Okay. 00:43:30.420 --> 00:43:37.510 position:50% align:middle I'm actually going to give a chance here for my colleague to speak because I'm noticing, like, 00:43:37.510 --> 00:43:41.440 position:50% align:middle we're getting to the point where he actually has his half hour. 00:43:41.440 --> 00:43:47.980 position:50% align:middle So I thank you very much for the questions, and by all means, keep them coming. 00:43:47.980 --> 00:43:52.000 position:50% align:middle Once again, after this presentation, I'll be in the booth over there where you might 00:43:52.000 --> 00:43:52.840 position:50% align:middle want to come. 00:43:52.840 --> 00:43:54.870 position:50% align:middle Just to reiterate, we've got candy. 00:43:54.870 --> 00:43:55.840 position:50% align:middle We've got Skittles. 00:43:55.840 --> 00:43:57.180 position:50% align:middle We have friendly people. 00:43:57.180 --> 00:43:58.500 position:50% align:middle So, thank you. 00:43:58.500 --> 00:43:59.727 position:50% align:middle Brendan.