Health Advocacy Training: Now Is the Time to Develop Physician Leaders
Bibliographic record
Abstract
To the Editor: Physicians have a societal responsibility to be health advocates and must act to positively influence public health and policy. But how can they achieve this without learning about the legislative process and policy reform? It is time for advocacy training to become more prominent in training future physicians, as the skills developed will enhance students' comprehension of how to deliver optimal patient care at the population level. Several regulatory physician organizations emphasize the importance of health advocacy training.1,2 At the medical school level, it is outlined in the AAMC's Medical School Objectives Project3 and in the Medical Council of Canada Qualifying Examination objectives.4 Several U.S. medical schools have incorporated health advocacy training into their curricula. For example, Cha et al5 developed a one-month curriculum in health activism where students research, develop, and implement an advocacy plan. However, Canadian medical schools have been slow to adopt similar programs. To address this deficiency, the University of Alberta and the University of Calgary hosted the first Annual Alberta Political Action Day (PAD) to engage medical students in political advocacy and policy reform. The 40 students participating received training from accomplished lobbyists and physicians and implemented it by meeting with over 41 elected representatives. The two-day requirement of this initiative makes PAD a good approach for medical schools to use to start integrating health advocacy training into their already busy curricula. Proficient health advocates will become leaders in health care by becoming health administrators, joining lobbying groups, or assisting charitable organizations. The need for stronger physician leaders has never been greater. Clearly, it is time for medical schools to place greater emphasis on health advocacy training. Peter J. Gill Medical student, MD/PhD Program, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada; [email protected]. Harbir S. Gill Medical student, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada. Thomas J. Marrie, MD Dean, Dalhousie Medical School, Dalhousie University, Halifax, Nova Scotia, Canada.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".