A Survey of Prelicensure Pain Curricula in Health Science Faculties in Canadian Universities
Bibliographic record
Abstract
OBJECTIVE: The present exploratory, descriptive study aimed to determine the designated time for mandatory pain content in curricula of major Canadian universities for students in health science and veterinary programs before being licensed.METHOD: Major Canadian university sites (n=10) were chosen where health science faculties included at least medicine (n=10) and nursing (n=10); many also included dentistry (n=8), pharmacy (n=7), physical therapy (n=8) and/or occupational therapy (n=6). These disciplines provide the largest number of students entering the workforce but are not the only ones contributing to the health professional team. Veterinary programs (n=4) were also surveyed as a comparison. The Pain Education Survey, developed from previous research and piloted, was used to determine total mandatory pain hours.RESULTS: The majority of health science programs (67.5%) were unable to specify designated hours for pain. Only 32.5% respondents could identify specific hours allotted for pain course content and/or additional clinical conferences. The average total time per discipline across all years varied from 13 h to 41 h (range 0 h to 109 h). All veterinary respondents identified mandatory designated pain content time (mean 87 h, range 27 h to 200 h). The proportion allotted to the eight content categories varied, but time was least for pain misbeliefs, assessment and monitoring/follow-up planning.CONCLUSIONS: Only one-third of the present sample could identify time designated for teaching mandatory pain content. Two-thirds reported ‘integrated’ content that was not quantifiable or able to be determined, which may suggest it is not a priority at that site. Many expressed a need for pain-related curriculum resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".