Defining core procedure skills for Canadian family medicine training.
Bibliographic record
Abstract
OBJECTIVE: To create a list of core and enhanced procedures suitable for family medicine training. DESIGN: Mailed or e-mailed survey using a Delphi technique. SETTING: Randomly selected family physician practices across Canada. PARTICIPANTS: Family physicians from urban, small-town, and rural practice locations and academic family physicians. All were experienced family physicians with from 3 to 36 years in practice. INTERVENTIONS: Participant physicians were asked to rate each of 158 procedures as to whether they would expect a graduate from a Canadian family practice training program to have learned and be capable of performing that procedure in their own community. In a second survey, participants were asked to verify the core and enhanced procedures lists produced from the first survey. MAIN OUTCOME MEASURES: Physicians' opinions about a comprehensive list of skills. RESULTS: Twenty-two physicians responded to the first survey (92% response rate) and 14 to the second (58% response rate). Sixty-five core procedures and 15 enhanced procedures were identified in the surveys. More procedures were ranked on the core list and were performed by rural and small-town physicians than by urban physicians. Physicians' agreement with placement of procedures on the core list ranged from 55% to 100% and of procedures on the enhanced list from 50% to 64%. Fifty-five of the procedures on the core list had agreement from more than 70% of participants. CONCLUSION: Procedure lists represent the opinions of Canadian family physicians about the importance of specific procedure skills for new family physicians in their communities. Procedure lists will be helpful for family medicine training programs to evaluate and refine their teaching of procedure skills.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".