COVID-19 disruption to family medicine residency curriculum: results from a 2020 US programme directors survey
Bibliographic record
Abstract
OBJECTIVE: This research project examined the effects of the COVID-19 pandemic on the required curriculum in graduate medical education for family medicine residencies. DESIGN: Our questions were part of a larger omnibus survey conducted by the Council of Academic Family Medicine Educational Research Alliance. Data were collected from 23 September to 16 October 2020. SETTING: This study was set in the USA. PARTICIPANTS: Emails were sent to 664 family medicine programme directors in the USA. Of the 312 surveys returned, 35 did not answer our questions and were excluded, a total of 277 responses (44%) were analysed. RESULTS: The level of disruption varied by discipline and region. Geriatrics had the highest reported disruption (median=4 on a 5-point scale) and intensive care unit had the lowest (median=1 on a 5-point scale). There were no significant differences for disruption by type of programme or community size. CONCLUSION: Programme directors reported moderate disruption in family medicine resident education in geriatrics, gynaecology, surgery, musculoskeletal medicine, paediatrics and family medicine site during the pandemic. We are limited in generalisations about how region, type of programme, community size or number of residents influenced the level of disruption, as less than 50% of programme directors completed the survey.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".