Exploring the perspectives of new-in-practice specialists about the Health Advocate role: “I didn’t even know where to start”
Bibliographic record
Abstract
Introduction: Current approaches to health advocate (HA) training leave many physicians feeling ill-equipped to advocate effectively. Likewise, faculty perceive the HA role as challenging to teach, role model, evaluate and assess. Progress on improving HA training is further stalled by debate over the role's importance and whether it should be considered intrinsic to medical practice. Recent graduates are well-positioned to comment on how these challenges affect HA training and preparation for practice. Therefore, our purpose was to explore the perspectives of new-in-practice physicians who are keen to be effective advocates. Methods: Ten early-career physicians participated in semi-structured interviews exploring their perceived competence and motivation to engage in health advocacy. Constructivist grounded theory informed the iterative data collection and analysis process. Results: Participants wished they knew during training how much they would use advocacy in practice. While training imparted adequate patient-level advocacy skills, participants felt underprepared to enact system-level advocacy-which they conceptualized as a wide-range of activities including political advocacy. In turn, participants grappled with lack of preparation, waning motivation, feelings of futility, lack of value for advocacy and need for self-preservation. For these reasons, they questioned whether system-level advocacy should be expected of all physicians. Conclusions: Although training may adequately prepare physicians for patient-level advocacy, system-level advocacy training remains insufficient. While patient-level advocacy is integral to good care, whether system-level advocacy should be a universal expectation deserves closer consideration. Perhaps system-level health advocacy may be better conceptualized as a specialized role requiring additional training. Regardless, physician advocates' efforts need to be valued for their contributions.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".