Health advocacy training: Why are physicians withholding life-saving care?
Bibliographic record
Abstract
The societal responsibility of physicians to be health advocates, both at the population and patient level is necessary to positively influence public health and policy. Physicians must commit to learn about policy reform and the legislative process. Several regulatory physician organizations emphasize the importance of health. In addition, the Association of American Medical Colleges' (AAMC) Medical Schools Objectives Project, the Medical Council of Canada Qualifying Examination objectives and several Canadian medical schools outline advocacy as an objective. As a result, several US medical schools have designed and incorporated health advocacy into their curricula. Canadian medical schools, however, have been lagging behind. To address this deficiency, the University of Alberta and the University of Calgary hosted the 1st Annual Alberta Political Action Day (PAD) to engage medical students in advocacy and the policy making process. The two-day time requirement of PAD makes it an efficient model to incorporate health advocacy into the already demanding undergraduate medical curriculum. Canadian medical schools must follow the American example and further integrate initiatives such as PAD to teach health advocacy. The skills developed will enhance student's comprehension of how they can shape health policy and truly advocate for optimal patient care.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".