WEBVTT 00:00:01.090 --> 00:00:06.120 position:50% align:middle - [Woman] Dr. Farag is a tenured associate professor at UI College of Nursing. 00:00:06.120 --> 00:00:11.950 position:50% align:middle She completed her Ph.D. and postdoctoral fellowship at Case Western Reserve University. 00:00:11.950 --> 00:00:19.190 position:50% align:middle Dr. Farag's research goals focus on understanding reactive and proactive approaches to enhance patient 00:00:19.190 --> 00:00:26.540 position:50% align:middle safety with an emphasis on safe medication administration practices across the care continuum. 00:00:26.540 --> 00:00:30.714 position:50% align:middle To achieve her research goals, Dr. Farag is currently collaborating with different 00:00:30.714 --> 00:00:36.684 position:50% align:middle interdisciplinary teams, and using machine learning and artificial intelligence 00:00:36.684 --> 00:00:45.415 position:50% align:middle to pursue novel approaches to studying measures to contain medication errors and enhance nurses' wellness. 00:00:52.018 --> 00:00:54.838 position:50% align:middle - [Dr. Farag] Hello, everyone. Thank you so much for being with me today. 00:00:54.838 --> 00:01:00.745 position:50% align:middle Today I will be presenting a study that was funded by the National Council State Board of Nursing. 00:01:00.745 --> 00:01:06.965 position:50% align:middle The title of my study is "Keeping Patients Safe: Evaluating Predictors of Nurse Fatigue and the 00:01:06.965 --> 00:01:12.295 position:50% align:middle Moderating Effects of Inter-shift Recovery." 00:01:12.295 --> 00:01:13.805 position:50% align:middle A little bit of a background. 00:01:13.805 --> 00:01:20.345 position:50% align:middle About 38% of working population in the United States suffer from occupational fatigue. 00:01:20.345 --> 00:01:27.755 position:50% align:middle Occupational fatigue is a multi-casual, multidimensional phenomenon that is intensified 00:01:27.755 --> 00:01:32.866 position:50% align:middle by excessive work demand and inadequate recovery. 00:01:32.866 --> 00:01:40.416 position:50% align:middle In fields outside the healthcare industry, employers and researchers try to put a quantifying 00:01:40.416 --> 00:01:43.216 position:50% align:middle amount or dollar amount to fatigue. 00:01:43.216 --> 00:01:50.756 position:50% align:middle So what we have in the literature so far, that employers base up to $136 billion annually 00:01:50.756 --> 00:01:57.966 position:50% align:middle in health-related loss to productive time and $45 billion annually in loss to productivity. 00:01:57.966 --> 00:02:05.981 position:50% align:middle Fatigued workers are subject to a lot of negative consequences such as musculoskeletal disorders, 00:02:05.981 --> 00:02:13.101 position:50% align:middle needlestick injuries, drowsy driving, accidents and near accidents, slow reaction time, 00:02:13.101 --> 00:02:18.191 position:50% align:middle altered cognitive function, and medication errors. 00:02:18.191 --> 00:02:21.731 position:50% align:middle Speaking of nurse fatigue, nurses are the largest professional group 00:02:21.731 --> 00:02:23.661 position:50% align:middle in healthcare setting. 00:02:23.661 --> 00:02:26.431 position:50% align:middle And this is not a new information for any of us. 00:02:26.431 --> 00:02:29.511 position:50% align:middle About 60% of nurses work in hospitals. 00:02:29.511 --> 00:02:33.178 position:50% align:middle According to some of the available national and international studies, 00:02:33.178 --> 00:02:37.958 position:50% align:middle 75% to 95% of nurses experience fatigue. 00:02:37.958 --> 00:02:41.448 position:50% align:middle Fatigue is more prevalent among females than male nurses. 00:02:41.448 --> 00:02:49.158 position:50% align:middle And in a study that monitored nurses and evaluated them for drowsy driving, this study reported that nurses 00:02:49.158 --> 00:02:59.029 position:50% align:middle in the study had 92 episodes of drowsy driving and 5 accidents or near accidents when they were monitored 00:02:59.029 --> 00:03:02.360 position:50% align:middle for a two-week period. 00:03:02.360 --> 00:03:05.560 position:50% align:middle Reviewing the literature reveals the following gaps. 00:03:05.560 --> 00:03:10.350 position:50% align:middle Whereas multiple studies were conducted to evaluate predictors of nurse fatigue, 00:03:10.350 --> 00:03:20.497 position:50% align:middle there are limited studies that proposed a comprehensive model to evaluate fatigue and fatigue predictors. 00:03:20.497 --> 00:03:29.718 position:50% align:middle There is also limited understanding of fatigue patterns within and between shifts or how the fatigue varies 00:03:29.718 --> 00:03:37.133 position:50% align:middle among nurses within the same shift or when they transition from one shift to the other. 00:03:37.133 --> 00:03:41.893 position:50% align:middle And there is also limited understanding of the relationship between nurse fatigue, medication error, 00:03:41.893 --> 00:03:46.813 position:50% align:middle and near miss and the moderating effect of inter-shift recovery. 00:03:46.813 --> 00:03:48.903 position:50% align:middle Finally, fatigue recovery measures. 00:03:48.903 --> 00:03:53.203 position:50% align:middle We have some evidence about some of the available measures for recovery, 00:03:53.203 --> 00:04:00.021 position:50% align:middle but we do not have a full understanding of recovery measures used by nurses while at work and 00:04:00.021 --> 00:04:03.161 position:50% align:middle during their off shifts. 00:04:03.161 --> 00:04:09.151 position:50% align:middle So guided by the System Engineering Initiative for Patient Safety, I conceptualized and 00:04:09.151 --> 00:04:11.341 position:50% align:middle operationalized my variables. 00:04:11.341 --> 00:04:22.421 position:50% align:middle And as you can see with the red titles, these is the variables that are used in my study. 00:04:22.421 --> 00:04:27.201 position:50% align:middle And this is the study model with all the variables that were measured. 00:04:27.201 --> 00:04:33.641 position:50% align:middle And this is the proposed model that I will be testing. 00:04:33.641 --> 00:04:39.893 position:50% align:middle So, about the method of the study. This is a multi-phased mixed method design. 00:04:39.893 --> 00:04:45.511 position:50% align:middle I used a quantitative and qualitative approach as well as ecological momentary assessment. 00:04:45.511 --> 00:04:52.771 position:50% align:middle The setting, I recruited nurses working in eight hospitals across one Midwestern state. 00:04:52.771 --> 00:04:58.421 position:50% align:middle For my sample, all nurses regardless of their age, experience, shifts, because most of the studies, 00:04:58.421 --> 00:05:04.331 position:50% align:middle and a lot of the studies focus on nurses working 12-hour shift or working specifically in night shift. 00:05:04.331 --> 00:05:07.801 position:50% align:middle But in my study, I invited all the nurses. 00:05:07.801 --> 00:05:15.941 position:50% align:middle My only exclusion criteria was for nurses who are in administrative position, agency and travel nurses, 00:05:15.941 --> 00:05:20.071 position:50% align:middle and nurses who do not provide direct patient care. 00:05:20.071 --> 00:05:30.124 position:50% align:middle I distributed 2029 survey. I received 1137 survey back. 00:05:30.124 --> 00:05:35.760 position:50% align:middle For my measures, I used already developed and standardized measures. 00:05:35.760 --> 00:05:40.904 position:50% align:middle I, of course, developed my personal and demographic questions. 00:05:40.904 --> 00:05:48.144 position:50% align:middle For the work environment, I included or I used the Practice Environment Scale and 00:05:48.144 --> 00:05:55.053 position:50% align:middle the revised Nursing Work Index to measure variables of the work environment such as leadership support, 00:05:55.053 --> 00:06:01.598 position:50% align:middle nurse/physician relationship, and staffing and resource adequacy. 00:06:01.598 --> 00:06:07.318 position:50% align:middle Sleep was measured by two scales, one is the Pittsburgh Sleep Quality and the other one 00:06:07.318 --> 00:06:15.009 position:50% align:middle was Epworth Daytime Sleepiness, and these are standardized and known measures used to evaluate sleep 00:06:15.009 --> 00:06:17.598 position:50% align:middle quality among nurses in prior studies. 00:06:17.598 --> 00:06:22.706 position:50% align:middle Similar to the previous studies, I used measures that has been used by prior researchers 00:06:22.706 --> 00:06:24.738 position:50% align:middle to evaluate fatigue. 00:06:24.738 --> 00:06:31.477 position:50% align:middle In addition to the standardized survey measures, I used ecological momentary assessment to evaluate the 00:06:31.477 --> 00:06:34.617 position:50% align:middle pattern of fatigue. 00:06:34.617 --> 00:06:40.517 position:50% align:middle So in this method, each nurse received four text every day, work and nonwork day, 00:06:40.517 --> 00:06:45.422 position:50% align:middle to rate their fatigue in a scale from 0 to 10. 00:06:45.422 --> 00:06:51.587 position:50% align:middle In the workdays, they were also asked to indicate if they have a medication error or a near miss and this 00:06:51.587 --> 00:06:56.117 position:50% align:middle was just yes-no question. 00:06:56.117 --> 00:07:00.224 position:50% align:middle My outcome variable of medication error and near miss, as I mentioned earlier, 00:07:00.224 --> 00:07:08.044 position:50% align:middle it was measured with the text messaging and also was measured as a single item question in the survey. 00:07:08.044 --> 00:07:15.554 position:50% align:middle So each nurse asked a question, if she had a medication error or near miss over the 00:07:15.554 --> 00:07:19.316 position:50% align:middle past months, and it was a dichotomous answer, yes, no. 00:07:19.316 --> 00:07:23.646 position:50% align:middle And nurses who said yes, they were asked to indicate whether it was 00:07:23.646 --> 00:07:28.344 position:50% align:middle the beginning, the middle, or the end of the shift. 00:07:28.344 --> 00:07:35.475 position:50% align:middle For the study procedure, as I mentioned earlier, this is a multi-phased study. 00:07:35.475 --> 00:07:37.835 position:50% align:middle So the first phase included the study survey. 00:07:37.835 --> 00:07:45.551 position:50% align:middle So the study IRB approval, it took eight months because IRB reviewers were not 00:07:45.551 --> 00:07:53.975 position:50% align:middle happy about asking the nurses to rate their fatigue and if they have a medication error or near miss. 00:07:53.975 --> 00:08:00.181 position:50% align:middle And the hospital leadership group considered this might be a liability issue for the hospitals. 00:08:00.181 --> 00:08:07.571 position:50% align:middle Anyway, after eight months of IRB review, I obtained the IRB approval. 00:08:07.571 --> 00:08:12.271 position:50% align:middle After obtaining the IRB approval, I attended all the nurses' staff meeting and I 00:08:12.271 --> 00:08:14.111 position:50% align:middle introduced the study. 00:08:14.111 --> 00:08:18.761 position:50% align:middle After the introduction of the study, I distributed the study survey 00:08:18.761 --> 00:08:21.061 position:50% align:middle to the nurses' mailboxes. 00:08:21.061 --> 00:08:26.741 position:50% align:middle Along with the study survey, I included an invitation for the second and third phase 00:08:26.741 --> 00:08:28.627 position:50% align:middle of the study. 00:08:28.627 --> 00:08:33.647 position:50% align:middle To improve the response rate, I distributed weekly reminder flyers and a last call 00:08:33.647 --> 00:08:37.257 position:50% align:middle flyer for over a three-week period. 00:08:37.257 --> 00:08:46.447 position:50% align:middle Each nurse received a $20 compensation upon receiving the completed survey. 00:08:46.447 --> 00:08:50.197 position:50% align:middle The second phase of the study, which is the text messaging or the 00:08:50.197 --> 00:08:52.997 position:50% align:middle ecological momentary assessment. 00:08:52.997 --> 00:08:57.687 position:50% align:middle Once I received the study survey back, I reviewed their individual invitation. 00:08:57.687 --> 00:09:04.227 position:50% align:middle Nurses who indicated their interest to be in the second phase of the study, I entered their cell phone number 00:09:04.227 --> 00:09:10.447 position:50% align:middle into a platform that was designed specifically for the study, then I called nurses to have their 00:09:10.447 --> 00:09:16.307 position:50% align:middle 14-day schedule, and then the 14-day schedule was entered into the texting platform. 00:09:16.307 --> 00:09:22.427 position:50% align:middle To avoid the disturbance of the workflow, I timed the texting. 00:09:22.427 --> 00:09:30.072 position:50% align:middle So the first text was 15 minutes before the beginning of the shift and the second and the last text was 15 00:09:30.072 --> 00:09:31.462 position:50% align:middle minutes at the end of the shift. 00:09:31.462 --> 00:09:38.522 position:50% align:middle So nurses had to reply to only two texts within the shift to rate their fatigue level. 00:09:38.522 --> 00:09:44.152 position:50% align:middle To improve a response rate for the text messaging, because nurses were monitored for 14-day period, 00:09:44.152 --> 00:09:51.652 position:50% align:middle which is a long period of time, participant were compensated $90 for completing the 00:09:51.652 --> 00:10:00.006 position:50% align:middle text and they received a $10 bonus if they answered at least 75% of their text back. 00:10:00.006 --> 00:10:02.066 position:50% align:middle And this approach was very successful. 00:10:02.066 --> 00:10:09.666 position:50% align:middle I only have 10 participant who didn't have a complete set of response. 00:10:09.666 --> 00:10:15.846 position:50% align:middle Initially, I had 1031 nurses indicated their interest to be in the second phase. 00:10:15.846 --> 00:10:27.575 position:50% align:middle However, 675 nurses were successfully enrolled, and as I mentioned, 10 of those didn't have a complete 00:10:27.575 --> 00:10:33.728 position:50% align:middle set of replies. That's why they were not included in the study. 00:10:33.728 --> 00:10:38.638 position:50% align:middle The third phase of the study included the qualitative interview. 00:10:38.638 --> 00:10:45.448 position:50% align:middle Before I did my qualitative interview, I had to do the analysis of the quantitative survey. 00:10:45.448 --> 00:10:51.168 position:50% align:middle And guided by their scores, I divided the participants into four quadrants of high 00:10:51.168 --> 00:10:58.178 position:50% align:middle fatigue and had medication error or a near miss, high fatigue with no medication error and a near miss, 00:10:58.178 --> 00:11:05.783 position:50% align:middle and then low fatigue and had medication error or a near miss, and low fatigue with no error or a near miss. 00:11:05.783 --> 00:11:16.223 position:50% align:middle This is maximum variation method to maximize the understanding of fatigue recovery and if nurses who 00:11:16.223 --> 00:11:24.143 position:50% align:middle recover better, they have lower chances of making medication error, and to see if there are any variation 00:11:24.143 --> 00:11:30.294 position:50% align:middle in the fatigue recovery measures used across nurses in the four quadrants. 00:11:30.294 --> 00:11:36.124 position:50% align:middle After I did these quadrants, I selected a random sample of nurses. 00:11:36.124 --> 00:11:41.764 position:50% align:middle I mailed…a total of 120 invitation were mailed to the nurses. 00:11:41.764 --> 00:11:47.224 position:50% align:middle Forty-two nurses replied back and were enrolled in the interview. 00:11:47.224 --> 00:11:55.674 position:50% align:middle Participants received a $40 compensation upon completing the qualitative interview. 00:11:55.674 --> 00:12:01.350 position:50% align:middle About the study result, for the sake of time, I will go over the descriptives. 00:12:01.350 --> 00:12:05.400 position:50% align:middle I'm not going to address the results of the regression analysis. 00:12:05.400 --> 00:12:12.140 position:50% align:middle Some of the results, I will share with you later on in the conclusion. 00:12:12.140 --> 00:12:19.880 position:50% align:middle Regarding the age, so the mean age of my sample was 35 which is a young age cohort. 00:12:19.880 --> 00:12:24.590 position:50% align:middle But when we look at the minimum and the maximum, you will notice that I have a wide 00:12:24.590 --> 00:12:30.159 position:50% align:middle range of participants. I range it between 20 to 72. 00:12:30.159 --> 00:12:36.549 position:50% align:middle Similar pattern was with the user experience and in the unit and with the nurse manager. 00:12:36.549 --> 00:12:39.269 position:50% align:middle I would like to direct your attention to the working hours. 00:12:39.269 --> 00:12:48.689 position:50% align:middle Although the mean was 35, but nurses, in my sample, worked between 3 to 73 hours per week. 00:12:48.689 --> 00:12:59.369 position:50% align:middle The one-way commute time ranged between 5 minute to 120 minute, and this is one-way commute per day. 00:12:59.369 --> 00:13:04.948 position:50% align:middle For social support, nurses perceive that they have some social support to some extent. 00:13:04.948 --> 00:13:10.688 position:50% align:middle Their mean score was 5.2 in a scale from 0 to 10. 00:13:10.688 --> 00:13:19.178 position:50% align:middle Regarding the other demographic variables, the majority of more than half of participant 00:13:19.178 --> 00:13:27.938 position:50% align:middle were married, has a PSN, work in critical care units, and 70% were full-time. 00:13:27.938 --> 00:13:32.952 position:50% align:middle And almost 60% of them had no children. 00:13:32.952 --> 00:13:36.552 position:50% align:middle For nurses who had children, I had to categorize their age, 00:13:36.552 --> 00:13:41.372 position:50% align:middle and I categorized their age based on the age of the youngest child. 00:13:41.372 --> 00:13:47.782 position:50% align:middle So almost one-quarter of the participant had a toddler living with them. 00:13:47.782 --> 00:13:51.852 position:50% align:middle And the majority of participant didn't have a secondary appointment. 00:13:51.852 --> 00:13:57.002 position:50% align:middle So they didn't work a second job. 00:13:57.002 --> 00:14:04.650 position:50% align:middle For the sleep, nurses had poor sleep quality and they had extensive daytime sleepiness. 00:14:04.650 --> 00:14:13.270 position:50% align:middle They suffered more acute fatigue than chronic, and mental fatigue than physical fatigue because a lot 00:14:13.270 --> 00:14:19.620 position:50% align:middle of nurses sometime complain, and some of the literature talk about how physically 00:14:19.620 --> 00:14:22.680 position:50% align:middle and mentally demanding is the nursing profession. 00:14:22.680 --> 00:14:28.530 position:50% align:middle I thought it would be equal, but here in my study, it was evident that they are more mentally 00:14:28.530 --> 00:14:30.977 position:50% align:middle than physically fatigued. 00:14:30.977 --> 00:14:34.397 position:50% align:middle And there are other study reported similar finding. 00:14:34.397 --> 00:14:45.044 position:50% align:middle For the inter-shift recovery, in a scale from 0 to 100, my participants reported 48, almost 49, 00:14:45.044 --> 00:14:50.498 position:50% align:middle which they are in the middle. They need to do a better job to recover. 00:14:50.498 --> 00:14:56.668 position:50% align:middle For the work environment, they were in the middle, so they evaluated leadership support, 00:14:56.668 --> 00:15:03.824 position:50% align:middle nurse/physician relationship, staffing and resource adequacy around the midpoint in a 00:15:03.824 --> 00:15:09.358 position:50% align:middle scale from 0 to 3. 00:15:09.358 --> 00:15:15.914 position:50% align:middle One of the study or of the strengths of this study was to evaluate pattern of nurse fatigue. 00:15:15.914 --> 00:15:21.484 position:50% align:middle And for this type of analysis, I collaborated with an engineer in the industrial 00:15:21.484 --> 00:15:24.514 position:50% align:middle engineering department in the University of Iowa. 00:15:24.514 --> 00:15:31.291 position:50% align:middle And we used one of the machine learning approaches, which is a Hidden Markov Model or 00:15:31.291 --> 00:15:33.821 position:50% align:middle Hidden Markov Modeling. 00:15:33.821 --> 00:15:39.361 position:50% align:middle To have a small clarification about what this model mean, or what this approach mean, 00:15:39.361 --> 00:15:44.011 position:50% align:middle or the use of this approach, this is a specific or a special case 00:15:44.011 --> 00:15:49.271 position:50% align:middle of cluster analysis, but it takes into consideration longitudinal data. 00:15:49.271 --> 00:15:54.471 position:50% align:middle So instead of clustering individual based on a specific construct or a variable, 00:15:54.471 --> 00:16:04.221 position:50% align:middle it cluster participant based on pattern or change, in my case, based on change in their fatigue pattern. 00:16:04.221 --> 00:16:10.571 position:50% align:middle The top level, as you can see here, has a smaller number of nurses and this 00:16:10.571 --> 00:16:12.701 position:50% align:middle is just accident. 00:16:12.701 --> 00:16:18.711 position:50% align:middle And the lower-three clusters has more participation. 00:16:18.711 --> 00:16:26.280 position:50% align:middle For the first top level, participant in Cluster 1, 2, and 3, they have close to a consistent 00:16:26.280 --> 00:16:27.261 position:50% align:middle state of fatigue. 00:16:27.261 --> 00:16:31.305 position:50% align:middle So Cluster 1 were at a low level of fatigue, and they remained low 00:16:31.305 --> 00:16:32.925 position:50% align:middle throughout the observation period. 00:16:32.925 --> 00:16:36.215 position:50% align:middle The second level were in the middle fatigue level. 00:16:36.215 --> 00:16:39.455 position:50% align:middle And they remained in the middle throughout the study period. 00:16:39.455 --> 00:16:47.535 position:50% align:middle And the third cluster had a high fatigue, and they remained high throughout the study period. 00:16:47.535 --> 00:16:52.635 position:50% align:middle However, this should be interpreted with caution because of the small number. 00:16:52.635 --> 00:16:57.915 position:50% align:middle The interesting three clusters, the ones that I am currently working on and doing some 00:16:57.915 --> 00:17:04.883 position:50% align:middle more analysis are Clusters 4, and 5, and 6, where there are some variation or the nurses fluctuate 00:17:04.883 --> 00:17:13.723 position:50% align:middle across three stages of fatigue, low, middle, and high levels of fatigue. 00:17:13.723 --> 00:17:21.133 position:50% align:middle To take a deeper dive into this pattern, and to make a small clarification, nurses in Cluster 4, 00:17:21.133 --> 00:17:23.863 position:50% align:middle they didn't reach a full recovery. 00:17:23.863 --> 00:17:30.678 position:50% align:middle So they were in the middle fatigue level and remained in the middle or moved to the high fatigue level. 00:17:30.678 --> 00:17:41.708 position:50% align:middle So they had a 40% chances of moving from middle to a high fatigue, and they have 27% chances of recovery. 00:17:41.708 --> 00:17:45.458 position:50% align:middle After adding the scheduling, because the scheduling is one of the possible 00:17:45.458 --> 00:17:49.768 position:50% align:middle intervention to manage fatigue and faster inter-shift recovery. 00:17:49.768 --> 00:17:57.318 position:50% align:middle So nurses in the middle level of fatigue, if they're going to work night shift the following day. 00:17:57.318 --> 00:18:06.618 position:50% align:middle They have 47%, almost 48% chances of building of fatigue and to move to a high fatigue level. 00:18:06.618 --> 00:18:10.218 position:50% align:middle Once they are at the high fatigue level, if they worked a second day, 00:18:10.218 --> 00:18:14.808 position:50% align:middle they need to work the second day off because if they're going to take an off the second day, 00:18:14.808 --> 00:18:19.718 position:50% align:middle they have 27% chances of recovery. 00:18:19.718 --> 00:18:25.148 position:50% align:middle Following the same logic, I'm sharing some data for nurses in Cluster 5 and 6. 00:18:25.148 --> 00:18:30.317 position:50% align:middle And what I would like to highlight here, that fatigue builds over time. 00:18:30.317 --> 00:18:38.457 position:50% align:middle So nurses do not move from first stage to high stage instantly, and this is where we need to tailor 00:18:38.457 --> 00:18:41.347 position:50% align:middle intervention to prevent the buildup of fatigue. 00:18:41.347 --> 00:18:51.557 position:50% align:middle So we would like to break the cycle to prevent nurses from moving the first stage to the second stage. 00:18:51.557 --> 00:18:56.257 position:50% align:middle Looking at the fatigue clusters in relation to medication error and near miss. 00:18:56.257 --> 00:19:05.513 position:50% align:middle Based on this result here, you can see that nurses in Cluster 3 and Cluster 4 had 00:19:05.513 --> 00:19:11.893 position:50% align:middle reported more medication error compared to the other, and had reported more near miss 00:19:11.893 --> 00:19:13.893 position:50% align:middle than the other clusters. 00:19:13.893 --> 00:19:19.213 position:50% align:middle If you recall nurses in Cluster 3 are the nurses who are at the high fatigue level and remained high 00:19:19.213 --> 00:19:22.003 position:50% align:middle throughout the observation period. 00:19:22.003 --> 00:19:28.663 position:50% align:middle And nurses in the Cluster Number 4 who stayed in second stage and third stage, 00:19:28.663 --> 00:19:36.201 position:50% align:middle they didn't have a chance to recover or they didn't recover throughout the 14-day observation period. 00:19:36.201 --> 00:19:44.951 position:50% align:middle So in conclusion, and this is where I will present a little bit of information about my regression analysis. 00:19:44.951 --> 00:19:49.781 position:50% align:middle Sleep quality and daytime sleepiness, caffeine consumption before work, 00:19:49.781 --> 00:19:56.351 position:50% align:middle staffing and resource adequacy were among the strongest predictor of various types of fatigue. 00:19:56.351 --> 00:20:01.378 position:50% align:middle I was surprised to have the caffeine consumption before work. 00:20:01.378 --> 00:20:07.308 position:50% align:middle I thought that during work will improve fatigue or decrease fatigue, but it turned to be that consuming it 00:20:07.308 --> 00:20:11.218 position:50% align:middle before work was more effective and significant predictor. 00:20:11.218 --> 00:20:17.718 position:50% align:middle Nurses are more mentally than physically fatigued and more acute than chronically fatigued. 00:20:17.718 --> 00:20:23.538 position:50% align:middle Surprisingly, day shift nurses were more fatigued than night shift nurses. 00:20:23.538 --> 00:20:26.448 position:50% align:middle But night shift delayed the nurse recovery. 00:20:26.448 --> 00:20:33.223 position:50% align:middle So night shift, it is not totally helpful for nurses' recovery. 00:20:33.223 --> 00:20:42.503 position:50% align:middle Caregiving responsibility and secondary job or having a second appointment was not associated with fatigue. 00:20:42.503 --> 00:20:51.623 position:50% align:middle And then finally, based on my qualitative analysis, the prevailing work culture of nurses prevented them 00:20:51.623 --> 00:20:56.553 position:50% align:middle from taking the necessary break during their work. 00:20:56.553 --> 00:21:00.546 position:50% align:middle So nurses shared with me that they know that they are tired, they are so fatigued, 00:21:00.546 --> 00:21:10.296 position:50% align:middle they're not able to keep their eyes open, but they didn't allow themselves to go to the breakroom 00:21:10.296 --> 00:21:17.376 position:50% align:middle to take a small break or even to think about taking a nap. 00:21:17.376 --> 00:21:22.616 position:50% align:middle This finding, I believe, it calls for the importance of teaching nurses, 00:21:22.616 --> 00:21:27.976 position:50% align:middle especially at the undergraduate level, the importance of well-being and how to take care 00:21:27.976 --> 00:21:32.280 position:50% align:middle of themself before taking care of their patients. 00:21:32.280 --> 00:21:38.085 position:50% align:middle And that's it. Thank you, and I'm open for any questions. 00:21:56.543 --> 00:22:00.473 position:50% align:middle Thank you, everyone, for joining me today in my presentation, 00:22:00.473 --> 00:22:04.612 position:50% align:middle and I am more than happy to entertain all your questions. 00:22:04.612 --> 00:22:11.882 position:50% align:middle A small update is since I finished the study, I submitted it now under review for... 00:22:11.882 --> 00:22:17.974 position:50% align:middle to the National Science Foundation, a grant proposal where I'll continue with my work 00:22:17.974 --> 00:22:21.392 position:50% align:middle with my industrial engineer collaborator, Dr. Yong Chen. 00:22:21.392 --> 00:22:30.166 position:50% align:middle And we are trying to develop a predictive model taking into consideration the shiftwork and the assignment 00:22:30.166 --> 00:22:32.928 position:50% align:middle of the nurses to predict their fatigue level. 00:22:32.928 --> 00:22:43.146 position:50% align:middle So, we'll see how the NSF will evaluate the application. 00:22:43.146 --> 00:22:47.616 position:50% align:middle So I'm looking here at the chatbox to see any questions. 00:22:47.616 --> 00:22:50.456 position:50% align:middle So far, nothing. 00:22:50.456 --> 00:22:57.166 position:50% align:middle So I will hold on tight. 00:22:57.166 --> 00:23:03.743 position:50% align:middle The other update other than the NSF foundation application, I'm still in the process 00:23:03.743 --> 00:23:07.373 position:50% align:middle of evaluating the clusters. 00:23:07.373 --> 00:23:15.733 position:50% align:middle So I will be meeting with the engineering team within like a month or so to start introducing all the 00:23:15.733 --> 00:23:25.324 position:50% align:middle different socio-demographic variables and some of the unit variable to see how this affect the pattern and 00:23:25.324 --> 00:23:30.914 position:50% align:middle the inter-shift recovery for nurses. So it is work in progress. 00:23:30.914 --> 00:23:34.434 position:50% align:middle So I have a question. 00:23:34.434 --> 00:23:37.854 position:50% align:middle How did they report the home duties? 00:23:37.854 --> 00:23:46.727 position:50% align:middle I am surprised those were not an the issue and unless the job was overwhelming. 00:23:46.727 --> 00:23:55.168 position:50% align:middle So I didn't ask them about the home duties, I just asked them if they have an older adult or a 00:23:55.168 --> 00:23:58.944 position:50% align:middle parent at home that they care of. And it was a yes-no question. 00:23:58.944 --> 00:24:05.546 position:50% align:middle And then I have the question about their children, and I categorized it by the age of the youngest child. 00:24:05.546 --> 00:24:08.691 position:50% align:middle But I didn't go in details about the home duties. 00:24:08.691 --> 00:24:13.976 position:50% align:middle Thank you so much for the question. It is a very valid one. 00:24:13.976 --> 00:24:19.447 position:50% align:middle So one interesting finding is, and it is in a manuscript that is 00:24:19.447 --> 00:24:25.431 position:50% align:middle currently in progress. Hopefully, it is so close to go out for review. 00:24:25.431 --> 00:24:32.895 position:50% align:middle Nurses who provide a caregiving responsibilities or assume caregiving responsibilities were not as more 00:24:32.895 --> 00:24:35.535 position:50% align:middle fatigued than the other nurses. 00:24:35.535 --> 00:24:40.175 position:50% align:middle Actually, in fact, they had a lower mental fatigue. 00:24:40.175 --> 00:24:42.565 position:50% align:middle So I was surprised by this finding. 00:24:42.565 --> 00:24:48.565 position:50% align:middle So I sent it back, sitting with my statistician, revising our codes, and reverse coding, and everything. 00:24:48.565 --> 00:24:58.595 position:50% align:middle And so far, everything sounds good and perfect. And it is a surprise to me. 00:24:58.595 --> 00:25:05.460 position:50% align:middle Another question, to see the 12-hour versus the 8-hour shift. 00:25:05.460 --> 00:25:09.720 position:50% align:middle Yes, this is another thing that I am looking at. 00:25:09.720 --> 00:25:14.780 position:50% align:middle With this machine learning approach, it is like so nice that it gives you a very deep and 00:25:14.780 --> 00:25:18.590 position:50% align:middle detailed understanding to what is going on there. 00:25:18.590 --> 00:25:24.076 position:50% align:middle So my next step is I'm going to split the 8 and the 12 and see. 00:25:24.076 --> 00:25:29.320 position:50% align:middle However, I may not be able to have a full understanding because I didn't have sufficient number of nurses who 00:25:29.320 --> 00:25:41.261 position:50% align:middle worked 8-hour shifts. The majority were 12-hour shifts. And I have some work 10-hour shifts. 00:25:41.261 --> 00:25:53.256 position:50% align:middle I have the ICU as one of the units and as… They showed a little bit higher acuity in the fatigue, 00:25:53.256 --> 00:25:59.901 position:50% align:middle the ICUs and the general units, but it didn't reach the significance level. 00:25:59.901 --> 00:26:08.649 position:50% align:middle And again, I will be introducing the ICU work into my pattern of fatigue to see if it will 00:26:08.649 --> 00:26:10.359 position:50% align:middle make any difference. 00:26:10.359 --> 00:26:15.109 position:50% align:middle So this is still ongoing, but in the general traditional analysis, 00:26:15.109 --> 00:26:19.659 position:50% align:middle the ICU didn't differ significantly from the other units. 00:26:19.659 --> 00:26:30.095 position:50% align:middle And then, great presentation, what data-specific…? 00:26:30.095 --> 00:26:32.935 position:50% align:middle Okay, so I have one more question. 00:26:32.935 --> 00:26:39.615 position:50% align:middle So there is one question about the specific, about the sleep pattern and the sleep 00:26:39.615 --> 00:26:41.425 position:50% align:middle problems like insomnia. 00:26:41.425 --> 00:26:50.423 position:50% align:middle No, the Pittsburgh and the Epworth will not help to identify insomnia, but it is in terms of the duration. 00:26:50.423 --> 00:26:57.805 position:50% align:middle It was evident that they have fewer hours of sleep and they have a high daytime sleepiness. 00:26:57.805 --> 00:27:03.981 position:50% align:middle So it is not necessarily insomnia per se. 00:27:03.981 --> 00:27:08.261 position:50% align:middle Fatigue level, of course, it can be related to job satisfaction but I didn't 00:27:08.261 --> 00:27:17.378 position:50% align:middle examine job satisfaction in this particular study. 00:27:17.378 --> 00:27:22.268 position:50% align:middle The hospitals that I worked with, they do not have any nap policy because the question 00:27:22.268 --> 00:27:23.771 position:50% align:middle was about having nap policies. 00:27:23.771 --> 00:27:28.785 position:50% align:middle The hospitals I collected data from, they do not have any. 00:27:31.691 --> 00:27:34.171 position:50% align:middle Oh, the nursing culture, it is very interesting one. 00:27:34.171 --> 00:27:39.471 position:50% align:middle And I address this in the qualitative manuscript that it is currently already published in the "Journal 00:27:39.471 --> 00:27:40.901 position:50% align:middle of Nursing Regulation." 00:27:40.901 --> 00:27:44.681 position:50% align:middle A small talk about the culture that nurses shared with me. 00:27:44.681 --> 00:27:46.131 position:50% align:middle They know that they are fatigued. 00:27:46.131 --> 00:27:50.001 position:50% align:middle They are not able to open their eye, but because of the workload or because they will feel 00:27:50.001 --> 00:27:55.691 position:50% align:middle guilty if they went to the breakroom, decided they do not want to go to the breakroom and 00:27:55.691 --> 00:28:01.111 position:50% align:middle ended up continuing working while they are fatigued. 00:28:04.088 --> 00:28:06.047 position:50% align:middle Thank you so much. 00:28:06.047 --> 00:28:13.388 position:50% align:middle Please feel free to contact me and to reach at amany-farag@uiowa.edu if you have 00:28:13.388 --> 00:28:14.168 position:50% align:middle any further question. 00:28:14.168 --> 00:28:18.608 position:50% align:middle And thank you so much for being here with me in my presentation. 00:28:18.608 --> 00:28:21.488 position:50% align:middle I greatly appreciate all your questions and feedback. 00:28:21.488 --> 00:28:25.773 position:50% align:middle Thank you, and have a wonderful day.