A Case Study of Student Wellness during A Longitudinal Integrated Clerkship Affected by System-level Disruption
Bibliographic record
Abstract
Objectives: Longitudinal Integrated Clerkships (LICs) promote educational continuity, professional identity formation, and community-based care, and are widely used in rurally focused medical education. While their academic resilience has been noted during systemic stress less is known about how such stress affects the personal lives and well-being of LIC students. Using the COVID-19 pandemic as a case study, we examined how disruption impacted student wellness. Methods: We conducted a mixed-methods case study using an anonymous online survey (n=32) and follow-up focus groups with students, faculty, and staff resulting in descriptive statistics for quantitative data while qualitative data was analysed thematically. Results: Students reported reduced physical activity, increased substance use, and worsening mental health. Barriers to care included stigma, lack of anonymity, and dual-role relationships in small communities. Many avoided formal supports, relying instead on peers and local administrative staff who felt overstretched and under-prepared. Central university services were often seen as remote or inaccessible. Seven themes emerged, relating to health impacts, support types, and the integration of central and local systems. Conclusions: The findings reveal a vulnerable underside to LIC placements. While academic learning continued, student wellness suffered. The personal cost of community-based training must be acknowledged if LICs are to meet their goals in workforce development and social accountability. Supporting student wellness is not just a moral duty, but a practical necessity. A hybrid model of local and centralized support, stigma reduction, and access to independent mental health care is essential to sustaining learners in community settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".