Mapping of pain curricula across health professions programs at the University of Toronto
Bibliographic record
Abstract
BACKGROUND: 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. AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".