Monitoring the integrity and usability of policy evaluation tools within an evolving socio-cultural 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. Keywords: program evaluation; health professions education; reflexive monitoring; competency-based education; healthcare policy
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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 | 0.631 | 0.728 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".