Impact of early waves of the COVID-19 pandemic on family medicine residency training
Bibliographic record
Abstract
OBJECTIVE: To identify how graduating and incoming family medicine residents (FMR) experienced changes to their education during the early waves of the COVID-19 pandemic. DESIGN: The Family Medicine Longitudinal Survey was modified with questions related to the impact of COVID-19 on FMR and their training. Short-answer responses underwent thematic analysis. Responses to Likert scale and multiple-choice questions were reported as summary statistics. SETTING: Department of Family and Community Medicine at the University of Toronto in Ontario. PARTICIPANTS: Graduating FMR in spring 2020 and incoming FMR in fall 2020. MAIN OUTCOME MEASURES: Residents' perceptions of the impact of COVID-19 on clinical skills acquisition and preparedness for practice. RESULTS: Surveys response rates were 124 of 167 (74%) and 142 of 162 (88%) for graduating and incoming residents, respectively. Important themes for both cohorts included reduced access to clinical environments, reduced patient volumes, and lack of exposure to procedural skills. While the graduating cohort indicated they felt confident to begin practising family medicine, they described being impacted by the loss of a tailored learning environment, including canceled or altered electives. In contrast, incoming residents reported the loss of core skills, such as physical examination competency, as well as the loss of face-to-face communication, rapport, and relationship-building opportunities. However, both cohorts endorsed gaining new skills during the pandemic, including conducting telemedicine appointments, pandemic planning, and interfacing with public health. CONCLUSION: Based on these results, residency programs can specifically tailor solutions and modifications to address common themes across cohorts to facilitate optimal learning environments in pandemic times.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".