Mapping of pain curricula across health professions programs at the University of Toronto
Bibliographic record
Abstract
There is a growing societal need for health professional competency in pain care. The University of Toronto Centre for the Study of Pain–Interfaculty Pain Curriculum (UTCSP-IPC) has been offered since 2002. Content and process have been updated annually. In addition, participating health professions programs have advanced their pain teaching. A curricular scan was needed to creatively and constructively advance the UTCSP-IPC. The aim of this study was to map curricular pain content in participating health professions programs onto the UTCSP-IPC content as a first step to further curriculum design. UTCSP-IPC committee members and faculty representatives from six health profession programs completed a 27-item online survey in this collaborative action study. Descriptive statistics were completed in Microsoft Excel. The UTCSP-IPC provided an average of 43.3% (range 32%–62%) of total pain content teaching hours to participating health professions students and a range of 8% to 100% of total opioid-related teaching hours. Curricular overlaps and gaps in pain content were identified and will be used to update and inform the iterative design of the UTCSP-IPC. Ninety-three percent of participating health professions faculty indicated that the interprofessional focus on pain care in the UTCSP-IPC was important. This study highlighted the value of the UTCSP and areas of curricular refinement to ensure continued relevance in relationship to pain content within the six participating health professions programs. Mapping a coordinated approach between uniprofessional and interprofessional teaching will both meet the demands of professional competence and create greater applicability to future practice settings.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".