Family physicians and health advocacy: Is it really a difficult fit?
Bibliographic record
Abstract
OBJECTIVE: in 2016 and to identify the perceived challenges and enablers of advocating across the entire spectrum. DESIGN: Analysis of a subset of data from a qualitative study using semistructured interviews and focus groups. SETTING: University of Toronto in Ontario. PARTICIPANTS: A total of 9 family medicine faculty members and 6 family medicine residents. METHODS: A subset of transcripts from a 2015 qualitative study that explored family medicine and psychiatry residents' and faculty members' understanding of the CanMEDS-Family Medicine health advocate role were reviewed, guided by interpretive descriptive methodology. MAIN FINDINGS: articles and that they valued the role. Further, there was widespread agreement that being a health advocate was linked with their identities as health professionals. The time it takes to be a health advocate was seen as a barrier to being effective in the role, and the work was seen as extremely challenging owing to system constraints. Participants also described a gap in training relating to advocacy at the system level as a challenge. CONCLUSION: Team-based care was seen as one of the most important enablers for becoming involved in the full spectrum of advocacy, as was time for personal reflection.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".