Systematic Review of Pain Medicine Content, Teaching, and Assessment in Medical School Curricula Internationally
Bibliographic record
Abstract
INTRODUCTION: Pain management is a major health care challenge in terms of the significant prevalence of pain and the negative consequences of poor management. Consequently, there have been international calls to improve pain medicine education for medical students. This systematic review examines the literature on pain medicine education at medical schools internationally, with a particular interest in studies that make reference to: a defined pain medicine curriculum, specific pain medicine learning objectives, dedicated pain education modules, core pain topics, medical specialties that teach pain medicine, elective study opportunities, hours allocated to teaching pain medicine during the curriculum, the status of pain medicine in the curriculum (compulsory or optional), as well as teaching, learning, and assessment methods. METHODS: A systematic review was undertaken of relevant studies on pain medicine education for medical students published between January 1987 and May 2018 using PubMed, Medline, Excerpta Medica database (EMBASE), Education Resources Information Center (ERIC), and Google Scholar, and Best Evidence Medical Education (BEME) data bases. RESULTS: Fourteen studies met the inclusion criteria. Evaluation of pain medicine curricula has been undertaken at 383 medical schools in Australia, New Zealand, the United States of America (USA), Canada, the United Kingdom (UK), and Europe. Pain medicine was mostly incorporated into medical courses such as anaesthesia or pharmacology, rather than presented as a dedicated pain medicine module. Ninety-six percent of medical schools in the UK and USA, and nearly 80% of medical schools in Europe had no compulsory dedicated teaching in pain medicine. On average, the median number of hours of pain content in the entire curriculum was 20 in Canada (2009), 20 in Australia and New Zealand (2018), 13 in the UK (2011), 12 in Europe (2012/2013), and 11 in the USA (2009). Neurophysiology and pharmacology pain topics were given priority by medical schools in all countries. Lectures, seminars, and case-based instruction were the teaching methods most commonly employed. When it was undertaken, medical schools mostly assessed student competency in pain medicine using written examinations rather than clinical assessments. CONCLUSIONS: This systematic review has revealed that pain medicine education at medical schools internationally does not adequately respond to societal needs in terms of the prevalence and public health impact of inadequately managed pain.
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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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".