Pain management training in undergraduate medical education
Bibliographic record
Abstract
The pain curriculum in medical education across the globe is lacking, leaving medical trainees ill prepared to properly assess and design plans to address acute and chronic pain. Poorly managed pain has implications on the individual in the form of psychological, physical, and financial costs, and on the greater healthcare system and economy. Gaps in education have resulted in suboptimal opioid prescribing habits contributing to the current opioid epidemic, and the development of negative attitudes towards patients with chronic pain amongst healthcare providers. Studies researching existing pain education in undergraduate medical education in North America, the United Kingdom, and Europe have identified limited pain teaching, typically incorporated into other courses rather than given a designated place in the curriculum. Several barriers to improving the provision of pain education have been identified, including resource limitations and perceived importance in comparison to other content. Improving pain education in Canada should be a priority given recent updates to the Canadian Guideline for Opioid Therapy and Chronic Noncancer Pain which recommends a decrease to the maximum dose of morphine. Implementing these guidelines will require physicians to have the knowledge and ability to safely taper patients whose opioid doses exceed the upper limit. Enhancing pain education will require an interdisciplinary approach with students developing competence not only in the identification and appropriate management of pain, but learning the communication and motivational interviewing skills to display empathy and compassion when providing care to the chronic pain patient population.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".