A Systematic Review of Pain Management Education in Graduate Medical Education
Bibliographic record
Abstract
Background: Despite the importance of pain management across specialties and the effect of poor management on patients, many physicians are uncomfortable managing pain. This may be related, in part, to deficits in graduate medical education (GME). Objective: We sought to evaluate the methodological rigor of and summarize findings from literature on GME interventions targeting acute and chronic non-cancer pain management. Methods: We conducted a systematic review by searching PubMed, MedEdPORTAL, and ERIC (Education Resources Information Center) to identify studies published before March 2019 that had a focus on non-cancer pain management, majority of GME learners, defined educational intervention, and reported outcome. Quality of design was assessed with the Medical Education Research Study Quality Instrument (MERSQI) and Newcastle-Ottawa Scale-Education (NOS-E). One author summarized educational foci and methods. Results: The original search yielded 6149 studies; 26 met inclusion criteria. Mean MERSQI score was 11.6 (SD 2.29) of a maximum 18; mean NOS-E score was 2.60 (SD 1.22) out of 6. Most studies employed a single group, pretest-posttest design (n=16, 64%). Outcomes varied: 6 (24%) evaluated reactions (Kirkpatrick level 1), 12 (48%) evaluated learner knowledge (level 2), 5 (20%) evaluated behavior (level 3), and 2 (8%) evaluated patient outcomes (level 4). Interventions commonly focused on chronic pain (n=18, 69%) and employed traditional lectures (n=16, 62%) and case-based learning (n=14, 54%). Conclusions: Pain management education research in GME largely evaluated chronic pain management interventions by assessing learner reactions or knowledge at single sites.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".