The Ethics of Canadian Entry‐To‐Practice Pain Competencies: How Are We Doing?
Bibliographic record
Abstract
BACKGROUND: Although unrelieved pain continues to represent a significant problem, prelicensure educational programs tend to include little content related to pain. Standards for professional competence strongly influence curricula and have the potential to ensure that health science students have the knowledge and skill to manage pain in a way that also allows them to meet professional ethical standards. OBJECTIVES: To perform a systematic, comprehensive examination to determine the entry-to-practice competencies related to pain required for Canadian health science and veterinary students, and to examine how the presence and absence of pain competencies relate to key competencies of an ethical nature. METHODS: Entry-to-practice competency requirements related to pain knowledge, skill and judgment were surveyed from national, provincial and territorial documents for dentistry, medicine, nursing, pharmacy, occupational therapy, physiotherapy, psychology and veterinary medicine. RESULTS: Dentistry included two and nursing included nine specific pain competencies. No references to competencies related to pain were found in the remaining health science documents. In contrast, the national competency requirements for veterinary medicine, surveyed as a comparison, included nine pain competencies. All documents included competencies pertaining to ethics. CONCLUSIONS: The lack of competencies related to pain has implications for advancing skillful and ethical practice. The lack of attention to pain competencies limits the capacity of health care professionals to alleviate suffering, foster autonomy and use resources justly. Influencing professional bodies to increase the number of required entry-to-practice pain competencies may ultimately have the greatest impact on education and practice.
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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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".