Resources for Educating, Training, and Mentoring All Physicians Providing Palliative Care
Bibliographic record
Abstract
This article presents a rapid review of the published literature and available resources for educating Canadian physicians to provide palliative and end-of-life care. Several key messages emerge from the review. First, there are many palliative care educational resources already available for Canadian physicians. Second, the many palliative care education resources are often not used in physician training. Third, we know that some palliative care educational interventions are inexpensive and scalable, while others are costly and time-consuming; we know very little about which palliative care educational interventions impact physician behavior and patient care. Fourth, two palliative care competency areas in particular can be readily taught: symptom management and communication skill (e.g., breaking bad news and advance care planning). Fifth, palliative care educational interventions are undermined by the "hidden curriculum" in medical education; interventions must be accompanied by continuing education and faculty development to create lasting change in physician behavior. Sixth, undergraduate and postgraduate medical training is shifting from a time-based training paradigm to competency-based training and evaluation. Seventh, virtually every physician in Canada should be able to provide basic palliative care; physicians in specialized areas of practice should receive palliative care education that is tailored to their area, rather than generic educational interventions. For each key message, one or more implications are provided, which can serve as recommendations for a framework to improve palliative care as a whole in 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".