ABSTRACT
Declining medical student attendance has been noted for decades, becoming particularly challenging in the years surrounding the COVID-19 pandemic. The reasons why medical students have not attended class in the years since the pandemic and the transition of USMLE Step 1 scores to Pass/Fail are not well established. This study evaluated why medical students did not attend a preclinical public health course. A secondary aim is to compare trends in self-reported attendance. Student course evaluations include self-reported attendance and free-text responses to ‘Why did you choose not to attend live sessions more often?’ Using inductive thematic analysis, faculty reviewers identified themes affecting attendance. The median self-reported attendance each year for first-year (MS1) and second-year (MS2) students was calculated and analyzed between 2018 and 2023. The overall evaluation response rate was 75.3% for MS1 and 59.3% for MS2. Five non-attendance themes emerged: academic schedule, learning style/delivery, content not valued, convenience/efficiency, and personal conflicts. MS2 attendance was significantly lower than MS1 attendance (p = 0.0227). Attendance varied significantly over time for both MS1 and MS2. MS1 had the highest percentage in 2018, which subsequently declined, with a nadir in 2021 (57.1%), before increasing again in 2022 (72.3%) and 2023 (75.9%) (p < 0.001). A similar pattern was observed for MS2 (p = 0.019), though with less rebound. Medical educators can adjust schedules and vary content delivery to impact some identified themes. Other themes, including convenience/efficiency and personal obligations, are more complex for educators to impact. Non-attendance is a multifaceted problem that escalated during the pandemic. MS2 visits rebounded less after the pandemic than for MS1. Step 1 grading changed during that time and may have played a role in attendance. This study adds to the sparse literature describing why MS1 and MS2 do not attend class. The identified themes provide a framework for further study. The effect of changing Step 1 scoring warrants further study.
KEYWORDS: Attendance, absence, curriculum development, USMLE Step 1, academic scheduling
Introduction
Declining medical school class attendance has been a persistent concern for decades [1], garnering increasing attention in recent years, particularly before the COVID-19 pandemic [2–4]. Between 2018 and 2020, the Association of American Medical Colleges (AAMC) reported that the percentage of second-year medical students (MS2) who reported ‘almost never’ attending lectures in person increased from 26% to 37% [5]. Educators have raised concerns about the potential impacts of nonattendance on learning [6,7] and professionalism [8,9]. Suggested explanations for this trend include increased flexibility in online learning [10], generational differences in learning preferences [11], and prioritization of USMLE Step 1 preparation [12].
Concerns about decreased student attendance among health professions students are not unique to medical schools. Absenteeism has also been a concern in nursing schools for at least 20 years [13]. Reasons given by medical and nursing students for voluntary absence in years prior to the pandemic include content delivery, individual learning style, availability of online lectures, personal commitments, schedule dissatisfaction, traveling long distances, and stress [13–16].
Despite widespread discussion, there is a paucity of published data evaluating the reasons medical students have not attended class since the COVID-19 pandemic and the subsequent transition of USMLE Step 1 to Pass/Fail in January 2022. One postpandemic study identified factors such as health concerns, family obligations, the timing of classes, teaching style, nonmandatory attendance, and difficulty readjusting to in-person learning [17]. A literature review revealed no published studies on the attendance-related impacts of the USMLE Step 1 transition to Pass/Fail.
Our urban mid-Atlantic medical school enrolled approximately 180 students each year during the period studied. Patients, Populations, and Systems (PPS) is a three-semester preclinical course series that has been part of the core curriculum since 2018. PPS covers epidemiology, biostatistics, social drivers of health, public health, health equity, health systems, quality improvement/patient safety, value-based care, and health policy. On average, PPS includes 1 or 2 one hour large group sessions per week (all students) and one 1.5 hour small group session per month (25 students each), with varying attendance policies over the years. ‘Live’ classes were held virtually but synchronously in 2020 and early 2021 due to the coronavirus disease 2019 (COVID-19) pandemic; these classes returned to largely in-person classes later in 2021 and fully in-person classes in 2022. Classes have remained so since then, with some asynchronous large group sessions that are completed independently. Attendance is tracked for the subset of sessions, which are labeled mandatory; typically, mandatory sessions include small group sessions and large group sessions with guest lecturers or patients. Most large group preclinical sessions, including PPS, are recorded for immediate upload to a secure website accessible to students. Viewing these recorded classes does not meet mandatory attendance requirements but does provide students access to the sessions. A minimum threshold of attendance is required to pass the course.
The purpose of this study is to elucidate why medical students chose not to attend live PPS classes between 2018 and 2023. A secondary aim is to analyze self-reported attendance trends among first- and second-year students (MS1 and MS2) over a five-year period that included the COVID-19 pandemic and the transition of USMLE Step 1 to Pass/Fail.
Methods
A PPS end-of-course evaluation questionnaire was emailed annually to all MS1 and MS2 students by the Office of Evaluation every December from 2018 to 2023. Participation was optional. Among other questions assessing PPS course content and pedagogy, the students reported how often they attended ‘live’ sessions by selecting one of five quartiles (0%, 1%−25%, 26%−50%, 51%−75%, 76%−100%). ‘Live’ was defined as synchronous online sessions during the COVID-19 pandemic but was otherwise defined as ‘in person or virtual.’ Beginning December 2021, respondents could also provide free-text responses to ‘Why did you choose not to attend live sessions (in person or virtual) more often?’.
We calculated the median self-reported attendance quartile for each calendar year for both MS1 and MS2. We performed the chi-square test of independence to determine whether a relationship exists between self-reported attendance and calendar year for MS1 and MS2 separately. We coded self-reported attendance quartiles as numbers (1−5) and compared MS1 and MS2 using mean differences to test for significance.
Free-text responses were analyzed using inductive thematic analysis. Two faculty members with expertise in medical education, independent of the PPS course (CB, GT), coded the responses separately and reconciled discrepancies through discussion to create one consistent codebook. A third reviewer (JC) resolved any remaining disagreements. Noncodable responses were excluded. The three (CB, GT, JC) then analyzed the codes to find emerging themes responsive to the research question. We calculated the percentage of codable responses that fell within each theme, both overall and by class year.
This study was declared exempt by the George Washington University Institutional Review Board (#041529).
Results
The questionnaire response rate for MS1 was 75.3% (834/1107, encompassing 2018−2023), and that for MS2 was 59.3% (530/894, encompassing 2019−2023). The median self-reported attendance is reported in Table 1.
Table 1.
Self-reported attendance at patients, populations, and systems class sessions among first- and second-year medical students from December 2018–2023.*
|
|
n | 0% | 1%–25% | 26%–50% | 51%–75% | 76%–100% | ||
|---|---|---|---|---|---|---|---|---|
| (%respond) | # (%) | # (%) | # (%) | # (%) | # (%) | Median Response | ||
| First-year students | 2018 | 156 (82.5) | 0 (0.0) | 2 (1.3) | 9 (5.8) | 14 (9.0) | 131 (84.0) | 76%–100% |
| 2019 | 128 (68.4) | 5 (3.9) | 17 (13.3) | 28 (21.9) | 31 (24.2) | 47 (36.7) | 51%–75% | |
| 2020 | 137 (77.0) | 2 (1.5) | 19 (13.9) | 23 (16.8) | 30 (21.9) | 63 (46.0) | 51%–75% | |
| 2021 | 163 (86.7) | 7 (4.3) | 26 (16.0) | 37 (22.7) | 51 (31.3) | 42 (25.8) | 51%–75% | |
| 2022 | 133 (73.6) | 7 (5.3) | 12 (9.0) | 18 (13.5) | 15 (11.4) | 81 (60.9) | 76%–100% | |
| 2023 | 116 (63.4) | 8 (6.9) | 11 (9.5) | 9 (7.8) | 14 (12.1) | 74 (63.8) | 76%–100% | |
| Second-year students | 2019 | 114 (61.3) | 2 (1.8) | 8 (7.0) | 21 (18.4) | 24 (21.1) | 59 (51.8) | 76%–100% |
| 2020 | 106 (58.2) | 8 (7.6) | 17 (16.0) | 14 (13.2) | 16 (15.1) | 51 (48.1) | 51%–75% | |
| 2021 | 109 (63.7) | 6 (5.5) | 22 (20.2) | 27 (24.8) | 22 (20.2) | 32 (29.4) | 26%–50% | |
| 2022 | 126 (72.0) | 6 (4.8) | 23 (18.3) | 28 (22.2) | 28 (22.2) | 41 (32.5) | 51%–75% | |
| 2023 | 75 (41.7) | 3 (4.0) | 13 (17.3) | 19 (25.3) | 11 (14.7) | 29 (38.7) | 51%–75% |
*In 2018 and 2019, all class sessions were in person. Due to COVID-19, ‘live’ sessions were delivered as synchronous virtual sessions in 2020 and 2021. Most ‘live’ class sessions were in person in 2022 and 2023, with a few synchronous virtual sessions. As a result, the survey questions were as follows: 2018 and 2019 – ‘How often did you attend class?’; 2020 and 2021 – ‘How often did you attend synchronous sessions?’; 2022 and 2023 – ‘How often did you attend live sessions (in person or virtual)?’.
Analysis of self-reported attendance revealed both cohort and calendar year effects. According to our linear model approach, MS2 patients reported significantly lower attendance than MS1 patients did (p = 0.0227). The chi-square test of independence showed significant variation in self-reported attendance over time for both groups. (Table 1 and Figures 1 and 2) MS1 had the highest level of attendance in 2018, which declined in 2019 and 2020, and it nadired in 2021 (when the median self-reported attendance was 51%−75%), before rebounding in 2022 and 2023 (p < 0.001). A similar pattern was observed for MS2 (p = 0.019), with a nadir in 2021 (with a median of 26%−50%) but a less notable rebound.
Figure 1.
Reported attendance at ‘live’ sessions by first-year students (MS1) for each year.
Figure 2.
Reported attendance at ‘live’ sessions by second-year students (MS2) for each year.
We received 298 total free-text responses between 2021 and 2023 (164 from MS1, 134 from MS2), with 55 adjudicated as noncodable because they did not address the question. The two coders agreed on codes for 230 responses (94.7%); the third coder was needed for disagreement on 13 responses (5.3%). Five themes emerged from the data: academic schedule, learning style or content delivery, content not valued, convenience/efficiency, and personal (non- or extracurricular) conflicts (Table 2). Table 3 shows the distribution of free-text responses falling within each theme for MS1 and MS2.
Table 2.
Themes that emerged through inductive qualitative analysis of responses from first- and second-year medical students describing why they did not attend class ‘live’.
| Theme | Example codes | Example response quotes |
|---|---|---|
| Convenience/Efficiency | View recording at faster speed | ‘Felt that it was more effective to watch them at 2x speed.’ |
| ‘I tend to prefer to watch lectures recorded at faster speeds to maximize efficiency.’ | ||
| Control over own schedule | ‘My schedule is busy and I prefer to watch the lectures when it works best for my schedule.’ | |
| ‘I usually preferred listening to the lecture later on while cooking, driving or exercising.’ | ||
| Avoid commute to school | ‘Travel time and cost’ | |
| ‘It is less convenient because I live far from campus.’ | ||
| Academic schedule | Time of day/day of week | ‘Classes are early in the morning.’ |
| ‘They were typically early in the morning on Fridays which I tended to use as my catch up days.’ | ||
| Many in-class hours | ‘Sometimes if back to back with multiple hours of lecture - it's hard for me to focus for that long and I know I would retain more later on recording.’ | |
| Prioritize work for other courses or exam prep | ‘A lot of the synchronous sessions were right before block exams and I wanted to use that time to study instead and watch the recorded lecture after’ | |
| ‘Particularly if a synchronous session preceded a major block exam, I would choose to skip the synchronous session so I could have more time to study for foundations/I3 exams.’ | ||
| Personal (non-curricular) conflict | Prioritize extracurricular activities | ‘I have other activities (shadowing/research) to do.’ |
| Personal obligations | ‘Personal conflicts/emergencies’ | |
| Illness/burnout | ‘Burnout from studying slides. I love PPS material and I'm passionate about the topics, but learning them via lecture/slides as opposed to active projects is difficult with a heavy course load.’ | |
| Content not valued | Curricular redundancy | ‘material was redundant from organ block lectures or prior PPS courses’ |
| Prior knowledge of content | ‘I have an MPH so that this material is very familiar.’ | |
| Perceived to be less important than other course content | ‘I prioritized course content such as I3 information over PPS.’ | |
| ‘PPS is not numerically graded and does not count towards the top 25% sentence on ERAS like our other preclinical classes do - it is therefore not a big priority.’ | ||
| Learning style/ content delivery |
Pause/rewind recording to self-pace | ‘I appreciated having the option to control the pace of sessions by watching recordings and having the ability to pause and restart lectures.’ |
| ‘Recorded lectures allow me to pause and take notes and understand concepts before moving on subsequent material. If I miss something in class then I stay lost the whole time without having an opportunity to clarify a concept before moving on.’ | ||
| Unengaging presentation | ‘The sessions were not engaging and so felt like a bit of a waste of time alongside all the other block work we had.’ | |
| Prefer home environment | ‘I do much better when I learn the material from home.’ |
Table 3.
Frequency of free-text responses given within each theme describing why first- or second-year medical students did not attend class ‘live’.*
| Theme | First-year students # (%) |
Second-year students # (%) |
Overall # (%) |
|---|---|---|---|
| Convenience/Efficiency | 55 (32.16%) | 58 (40.28%) | 113 (35.87%) |
| Academic schedule | 72 (42.11%) | 46 (31.94%) | 118 (37.46%) |
| Personal (non-curricular) conflict | 20 (11.70%) | 9 (6.25%) | 29 (9.21%) |
| Content not valued | 21 (12.28%) | 17 (11.81%) | 38 (12.06%) |
| Learning style or delivery | 44 (25.73%) | 35 (24.31%) | 79 (25.08%) |
*Values sum to more than 100% because respondents could give multiple answers.
Discussion
Attendance trends
Analysis of self-reported attendance revealed varied by cohort and calendar year. Overall, self-reported MS2 attendance was significantly lower than MS1 attendance. Additionally, although both the MS1 and MS2 cohorts experienced a rebound in attendance after declining during the pandemic, the rebound for MS2 attendance was not as robust as that for MS1. The first year in which the median self-reported attendance quartile for MS2 was lower than that for MS1 was 2021, at which time it has remained lower. Step 1 became passed/failed in January 2022; we believe that this change may be a likely contributor. Further research is needed before conclusions about the impact of the Step 1 scoring change on attendance can be drawn.
Attendance for both MS1 and MS2 began declining in 2019 and was at its lowest level in 2021, when live sessions were synchronous virtual sessions. Low attendance in the face of virtual session delivery suggests that although commute and travel inconvenience may play a role in attendance decisions, other factors also impact nonattendance. For example, students may value the ability to control the time of day for instruction, to replay portions of the session, and to vary the playback speed of recordings. Asynchronous sessions are often considered a potential solution to address non-attendance; however, our internal data on the unique viewership of asynchronous-only sessions during this timeframe show that ‘attendance’ was highly variable and generally low despite the asynchronous modality. (data not shown.) This suggests that themes such as learning style/content delivery or content not valued may also impact asynchronous viewership.
Thematic analysis
The themes that emerged in our study provide insights into the complex factors influencing medical students’ decisions to attend live classes: academic schedule, learning style/content delivery, content not valued, convenience/efficiency, and personal conflicts. These themes largely reinforce factors that have been presented in prior studies of attendance among health profession students. A 2013 study of medical student attendance noted that the primary driver for attendance was the quality of the lecturer [15]. While the lecturer’s delivery of course content was an important theme in our results, academic schedule and convenience/efficiency were the most frequent themes in our study.
Some themes can be influenced by medical educators and medical school program leadership, while others cannot. Those that medical educators and leadership can influence include the academic schedule, learning style/content delivery, and content not valued.
With respect to academic scheduling, the responses suggest that the time of day, specifically 8:00 am classes, scheduling close to an exam, or scheduling on days with many total in-class hours, reduced the likelihood that students would attend a live session. While these aspects of the schedule are within the control of medical school leadership, the volume of content covered during the preclinical curriculum limits schedule flexibility.
Responses to the learning style/content delivery theme highlight the importance to students of creating engaging content that appeals to a variety of learning styles, as has been previously reported [11,15,18]. Adult learners in medical schools are likely to have a variety of preferred methods of content delivery; while some delivery methods, such as panels and workshops, may appeal to more students, making it unlikely to find a method that appeals to all students. A 2021 study of college students revealed that, in the face of competing priorities, students viewed attendance tactically and even missed lectures that they believed would be interactive and enjoyable when faced with competing external forces [19]. This could mean that attendance at nonmandatory courses may have a natural limit as students prioritize classes to attend.
With respect to the perceived value of the course content, the responses suggested that some students felt that PPS course material was less valuable to them than basic science material. Educators may be able to influence this perception by explicitly emphasizing the importance of understanding social drivers of health, health systems, quality improvement and health policy for all practicing physicians. Early clinical experiences that highlight these topics could also help shape students’ perceptions of these topics as relevant to their future practice.
The themes that are more difficult for educators to influence include convenience/efficiency and personal (noncurricular or extracurricular) conflicts. Convenience/efficiency covers a broad range of codes, including the length of the commute and the desire for a faster playback speed of recorded lectures. The responses demonstrate that many students use recordings in an interactive way, pausing or replaying for topics that are more difficult to understand and otherwise playing at increased playback speed. It is generally asserted that technology-native generations, including the Millennial Generation and Generation Z, became accustomed to using technology early in their educational careers and, therefore, may engage with technology differently than the faculty from previous generations, who are largely responsible for developing their curricula [11,20].
While the current generation of medical students’ use of technology to enhance or replace in-person class attendance may be consistent with their preexisting use of technology for educational purposes, additional research is needed to guide medical educators in best practices on the use and scope of technology. For example, it is unclear whether listening to recordings at increased speed impacts students’ ability to retain information. It is also unclear whether students’ long-term use of educational technology helps them recognize when they need to slow or repeat information versus when they can resume faster speeds. Additional research is needed on best practices for the integration of recorded lectures to allow educators to better counsel this population of adult learners on learning strategies and to address potential challenges, such as impacts on the development of social skills, which are crucial for professional development as future physicians. Furthermore, additional studies could identify the reasons why students may choose to use a faster playback speed, such as the quality and style of content delivery, which can potentially be impacted by medical educators.
Another theme over which educators may have limited influence, personal conflicts, conflicts with extracurricular activities and issues related to students’ healthcare or family concerns. This raises the question of whether students prioritize extracurricular engagement across classes. Students may increasingly value extracurricular achievements over coursework in response to the new landscape of applying to residency without a Step 1 numerical score. In the first National Residency Match Program (NRMP) annual survey of residency program directors across 23 specialties after the transition from Step 1 to Pass/Fail scoring, 70% of program directors noted that demonstrating leadership in one’s application was extremely important or very important; 40% said the same about community service, and 19.3% and 15.1% said the same about specialty-specific research and research publications, respectively [21]. This suggests that students choosing to prioritize extracurricular activities may be justified in attempting to position themselves for a successful residency match. While this theme represented a small number (8/245) of our codable responses, this potential effect of the Step 1 scoring change on class attendance would benefit from future research.
Strength and limitations
The strengths of this study include that the data spanned six academic years and included responses from at least 834 unique students (total responses from the MS1 cohort). Additionally, the thematic analysis reached saturation. The free-text nature of survey responses and subsequent inductive analysis provided an opportunity to hear directly from medical students about what is driving their decisions around class attendance. PPS covers some topics that are tested on Step 1 (e.g. study design, epidemiology, and biostatistics) as well as others that are not (e.g. social drivers of health, health policy, health insurance), which potentially limits generalizability to courses that focus on more traditional medical knowledge or USMLE content; however, four of the five themes that emerged were not directly related to course content. This suggests that our findings may inform approaches to encouraging attendance in other medical school courses as well.
This study has several limitations. Although the response rate from MS1 was fairly good at 75.3%, that for MS2 was only 59.3%. This decrease in response rate is consistent with the decline observed for other course evaluations at our institution. The respondents may not be representative of the entire cohort; using a voluntary end-of-course evaluation may unintentionally sample students with higher attendance levels. The broad spread of quantitative responses, however, shows that students from all quartiles of self-reported attendance were sampled, which may suggest less impact of selection bias. Additionally, the nature of free-text written responses limits qualitative insights compared to interviews. However, the responses did provide some depth, with 42% of the codable responses generating 2 or more codes and only 18.3% of the total responses being noncodable. Because the responses were gathered from an evaluation of related courses at one institution, generalizability may be limited. Further study across multiple institutions and courses would increase the generalizability of the results. Finally, changes that occurred over the timeframe of sampling may have impacted the results. During the timeframe over which the responses were gathered, our school transitioned from a tiered grading system to exclusively Pass/Fail grades for preclinical courses. Attendance policies, including mandatory attendance at individual class sessions, also varied somewhat during this time frame. Differences between sessions being mandatory or not being mandatory may impact student attendance; in fact, several students specifically noted in free-text comments that they attended mandatory sessions. We did not explore this potential difference in this study, which used self-reported attendance across the entire course (i.e. mandatory and nonmandatory sessions). Given that the majority of class sessions at our and other medical schools are not typically mandatory, we believe that including all sessions makes our results more generalizable to general student behavior. Finally, the COVID-19 pandemic caused this curriculum to employ all virtual learning from March 2020–May 2021. To account for this, the wording of the question was changed slightly to reflect attendance at synchronous, virtual sessions during that time, which is the nearest parallel for in-class attendance. However, the reasons for nonattendance at virtual sessions may differ from those for nonattendance at in-person classes.
Future research
This preliminary study adds to very few studies describing why medical students have not attended preclinical classes since the COVID-19 pandemic and following the transition of Step 1 to Pass/Fail. Further study of this topic should include surveying medical students across multiple institutions and courses.
To inform actionable steps that may increase attendance, students should be queried on what encourages them to attend class in the era of the Pass/Fail Step 1 examination. Our free-text question, ‘Why did you choose not to attend live sessions.?’ (italics added) provided responses to inform this discussion, but we did not directly ask students what they believe would increase their attendance. Further studies are needed to draw stronger conclusions related to this issue. Research shows that students’ self-reported attendance is higher than their actual attendance level [22]. The reasons for this discrepancy may be more complex than initially perceived; one study of college students showed that simply notifying students of their cumulative attendance improved attendance at nonmandatory anatomy classes [23]. One small but promising 2021 study of nursing students demonstrated the potential effectiveness of awarding digital badges for attendance as a form of educational gaming to increase motivation [24]. These two studies highlight the potential for innovative approaches to incentivize attendance using positive rather than punitive approaches. Further research is warranted to inform best practices that deliver demonstrated impact on medical student attendance.
Acknowledgments
We would like to thank Tom Harrod, Research Support Librarian and Adjunct Instructor of Medicine, GW SMHS, for his help in performing a literature search to inform this work. We would like to thank Sean Lee, PhD, Office of Clinical Research, for his help in performing quantitative statistical analysis for this study and creating the figures.
Disclosure statement
No potential conflict of interest was reported by the author(s).
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