Content analysis of chronic pain content at three undergraduate medical schools in Ontario
Bibliographic record
Abstract
Background: It has been well documented that interdisciplinary, comprehensive pain education can foster positive pain beliefs among medical students, in addition to improving students’ abilities to diagnose and treat pain. Though some work has been done to quantify the number of hours of pain education students receive, the content itself has received little attention.Aims: This study seeks to identify what medical students learn about chronic pain throughout an undergraduate medical degree program in Ontario.Methods: Three undergraduate medical schools in Ontario were selected on the basis of variety in curricular structure and instructional methods. Written documents comprising the formal curriculum were analyzed through qualitative and quantitative content analysis. These findings were compared with promising practices from the pain education literature.Results: The three curricula studied here dedicate the bulk of pain education to three topics: pain mechanisms, pain management, and opioids and addiction. The curricula vary considerably in organization of content and hours of pain training. All three curricula were found to contain negative pain beliefs that characterize pain patients as difficult, overwhelming, and unrewarding to work with. Two of the medical schools studied here do not have a pain curriculum.Conclusions: The results of this study indicate a need for medical schools to develop comprehensive, interdisciplinary pain curricula. Though increasing the number of hours of pain training is crucial, equally imperative is a consideration of what, and how, students learn about pain.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".