Monitoring the integrity and usability of policy evaluation tools within an evolving sociocultural context: A demonstration of reflexivity using the CFPC Family Medicine Longitudinal Survey
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Over the last decade, policy changes have prompted Canadian medical education to emphasize a transformation to competency-based education, and subsequent development of evaluation tools. The pandemic provides a unique opportunity to emphasize the value of reflexive monitoring, a cyclical and iterative process of appraisal and adaptation, since tools are influenced by social and cultural factors relevant at the time of their development. METHODS: Deductive content analysis of documents and resources about the advancement of primary care. Reflexive monitoring of the Family Medicine Longitudinal Survey (FMLS), an evaluation tool for physician training. RESULTS: The FMLS tool does not explore all training experiences that are currently relevant; including, incorporating technology, infection control and safety, public health services referrals, patient preferences for care modality, and trauma-informed culturally safe care. CONCLUSION: The results illustrate that reflection promotes the validity and usefulness of the data collected to inform policy performance and other initiatives.
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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 | 0.682 | 0.762 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".