Expectations of Clinical Teachers and Faculty Regarding Development of the CanMEDS-Family Medicine Competencies: Laval Developmental Benchmarks Scale for Family Medicine Residency Training
Bibliographic record
Abstract
BACKGROUND: The CanMEDS-Family Medicine (CanMEDS-FM) framework defines the expected terminal enabling competencies (EC) for family medicine (FM) residency training in Canada. However, benchmarks throughout the 2-year program are not yet defined. PURPOSES: This study aimed to identify expected time frames for achievement of the CanMEDS-FM competencies during FM residency training and create a developmental benchmarks scale for family medicine residency training. METHODS: This 2011-2012 study followed a Delphi methodology. Selected faculty and clinical teachers identified, via questionnaire, the expected time of EC achievement from beginning of residency to one year in practice (0, 6, 12, […] 36 months). The 15-85th percentile intervals became the expected competency achievement interval. Content validity of the obtained benchmarks was assessed through a second Delphi round. RESULTS: The 1st and 2nd rounds were completed by 33 and 27 respondents, respectively. A developmental benchmarks scale was designed after the 1st round to illustrate expectations regarding achievement of each EC. The 2nd round (content validation) led to minor adjustments (1.9±2.7 months) of intervals for 44 of the 92 competencies, the others remaining unchanged. CONCLUSIONS: The Laval Developmental Benchmarks Scale for Family Medicine clarifies expectations regarding achievement of competencies throughout FM training. In a competency-based education system this now allows identification and management of outlying residents, both those excelling and needing remediation. Further research should focus on assessment of the scale reliability after pilot implementation in family medicine clinical teaching units at Laval University, and corroborate the established timeline in other sites.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".