Family medicine residents' perspectives on curricular messaging surrounding enhanced skills fellowship programs.
Bibliographic record
Abstract
OBJECTIVE: To better understand the messages that family medicine residents receive about enhanced skills fellowship programs throughout their training. DESIGN: Phenomenologic approach using structured qualitative interviews. SETTING: Postgraduate family medicine program in Ontario. PARTICIPANTS: Eleven family medicine residents (5 first-year and 6 second-year residents) from 4 separate training sites. METHODS: Interviews were audiotaped and codes were developed by the study investigators. Themes arose from the data via the immersion and crystallization technique. MAIN FINDINGS: Themes emerged in 3 categories: perception of purpose, sources of messaging, and formal or informal versus hidden curricular messages. Fellowship programs were viewed by residents in terms of their personal and professional benefits. Residents learned about fellowship programs through word of mouth and from role models. The formal curriculum remained neutral about fellowship training. The hidden curriculum highlighted a number of messages: a) to maximize chances of acceptance into some fellowship programs, one should focus most of his or her elective time in that clinical area; b) many fellowships graduate subspecialists to the exclusion of family medicine; c) a fellowship is required to practise in a large urban centre but is not required to practise in rural communities; and d) those without fellowship training are less well regarded. CONCLUSION: Residents receive mixed messages regarding fellowship training. This might be a phenomenon isolated to a larger urban centre in Ontario. Decision making at the individual level in terms of career path seems to be affected and might have implications at the larger system level.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".