Fundamental components of a curriculum for residents in health advocacy
Bibliographic record
Abstract
PURPOSE: To develop components of a curriculum for teaching and evaluating Residents as health advocates. METHOD: Modeled on the Delphi technique, the first step involved a multidisciplinary panel of 10 Queen's University health care providers with expertize in education and patient advocacy. In the context of four Advocacy questions: What is it?, Who does it?, How to teach it?, and How to evaluate it?, they discussed a curriculum framework including graded education, scholarly activity, role modeling, and case examples. In the second step, 24 faculty experts addressed two goals: (1) to identify attributes discussed by the expert panel in step 1 and corresponding measurable behaviours and (2) to refine the curriculum framework proposed in step 1 with emphasis on content and evaluation. RESULTS: Six attributes of a health advocate were identified; knowledgeable, altruistic, honest, assertive, resourceful, and up-to date. Behaviours that reflect these attributes were identified as desirable or undesirable and means of teaching were matched to the attributes. For most residents, skills would be developed in a graded fashion, progressing from advocating for the individual to society as a whole. CONCLUSIONS: This study provides a general framework from which specialty-specific curriculums for training health advocates can be developed.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".