Getting the job done: educational robustness of a multi-campus Longitudinal Integrated Clerkship during the COVID-19 pandemic
Bibliographic record
Abstract
Background. The COVID-19 pandemic profoundly impacted medical education worldwide, leading to challenges and adaptation of both in class and clinical education models. The Longitudinal Integrated Clerkship (LIC) emphasizes community engagement and integration in rural and remote communities making it a unique model to evaluate adaptability during the pandemic. This study examines how the pandemic affected the academic and clinical experiences of third-year medical students engaged in an LIC situated in Northern Ontario. Methods. The study employed an anonymous survey completed by 32 LIC students and 18 program administrators. Data collection focused on non-clinical and clinical learning activities, COVID-19 experiences, and the implementation of virtual care. Results. Despite pandemic challenges, 72% of program administrators reported the overall quality of education remained consistent with previous years. All students successfully met required clinical learning objectives and other promotion requirements, although 56% reported restricted clinical access and limited experience with specialists. Virtual care became a primary adaptation, with 97% of students participating. Student safety was supported by adequate personal protective equipment availability, and the program maintained continuity thoughout the disruption. Conclusions. The LIC demonstrated resilience, maintaining educational quality during the pandemic. These findings highlight the strengths of LIC model in terms of adaptability and program continuity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".