Giving curriculum planners an edge: using entrance surveys to design family medicine education.
Bibliographic record
Abstract
OBJECTIVE: To pilot a survey of family medicine residents entering residency, describing their exposure to family medicine and their perspectives related to their future intentions to practise family medicine, in order to inform curriculum planners; and to test the methodology, feasibility, and utility of delivering a longitudinal survey to multiple residency programs. DESIGN: Pilot study using surveys. SETTING: Five Canadian residency programs. PARTICIPANTS: A total of 454 first-year family medicine residents were surveyed. MAIN OUTCOME MEASURES: Residents' previous exposure to family medicine, perspectives on family medicine, and future practice intentions. RESULTS: Overall, 70% of first-year residents surveyed responded (n = 317). Although only 5 residency programs participated, respondents included graduates from each of the medical schools in Canada, as well as international medical graduates. Among respondents, 92% felt positive or strongly positive about their choice to be family physicians. Most (73%) indicated they had strong or very strong exposure to family medicine in medical school, yet more than 40% had no or minimal exposure to key clinical domains of family medicine like palliative care, home care, and care of underserved groups. Similar responses were found about residents' lack of intention to practise in these domains. CONCLUSION: Exposure to clinical domains in family medicine could influence future practice intentions. Surveys at entrance to residency can help medical school and family medicine residency planners consider important learning experiences to include in training.
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 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 | 0.031 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".