Psychometric validation of the Laval developmental benchmarks scale for family medicine
Bibliographic record
Abstract
BACKGROUND: With the implementation of competency-based education in family medicine, there is a need for summative end-of-rotation assessments that are criterion-referenced rather than normative. Laval University's family residency program therefore developed the Laval Developmental Benchmarks Scale for Family Medicine (DBS-FM), based on competency milestones. This psychometric validation study investigates its internal structure and its relation to another variable, two sources of validity evidence. METHODS: We used assessment data from a cohort of residents (n = 1432 assessments) and the Rasch Rating Scale Model to investigate its reliability, dimensionality, rating scale functioning, targeting of items to residents' competency levels, biases (differential item functioning), items hierarchy (adequacy of milestones ordering), and score responsiveness. Convergent validity was estimated by its correlation with the clinical rotation decision (pass, in difficulty/fail). RESULTS: The DBS-FM can be considered as a unidimensional scale with good reliability for non-extreme scores (.83). The correlation between expected and empirical items hierarchies was of .78, p < .0001.Year 2 residents achieved higher scores than year 1 residents. It was associated with the clinical rotation decision. CONCLUSION: Advancing its validation, this study found that the DBS-FM has a sound internal structure and demonstrates convergent validity.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".