Grappling with key questions about assessment of the Health Advocate role
Bibliographic record
Abstract
Introduction: Although the CanMEDS framework sets the standard for Canadian training, health advocacy competence does not appear to factor heavily into high stakes assessment decisions. Without forces motivating uptake, there is little movement by educational programs to integrate robust advocacy teaching and assessment practices. However, by adopting CanMEDS, the Canadian medical education community endorses that advocacy is required for competent medical practice. It's time to back up that endorsement with meaningful action. Our purpose was to aid this work by answering the key questions that continue to challenge training for this intrinsic physician role. Methods: We used a critical review methodology to both examine literature relevant to the complexities impeding robust advocacy assessment, and develop recommendations. Our review moved iteratively through five phases: focusing the question, searching the literature, appraising and selecting sources, and analyzing results. Results: Improving advocacy training relies, in part, on the medical education community developing a shared vision of the Health Advocate (HA) role, designing, implementing, and integrating developmentally appropriate curricula, and considering ethical implications of assessing a role that may be risky to enact. Conclusion: Changes to assessment could be a key driver of curricular change for the HA role, provided implementation timelines and resources are sufficient to make necessary changes meaningful. To truly be meaningful, however, advocacy first needs to be perceived as valuable. Our recommendations are intended as a roadmap for transforming advocacy from a theoretical and aspirational value into one viewed as having both practical relevance and consequential implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".