From Opiophobia to Overprescribing: A Critical Scoping Review of Medical Education Training for Chronic Pain
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a significant health problem strongly associated with a wide range of physical and mental health problems, including addiction. The widespread prevalence of pain and the increasing rate of opioid prescriptions have led to a focus on how physicians are educated about chronic pain. This critical scoping review describes the current literature in this important area, identifying gaps and suggesting avenues for further research starting from patients' standpoint. METHODS: A search of the ERIC, MEDLINE, and Social Sciences Abstracts databases, as well as 10 journals related to medical education, was conducted to identify studies of the training of medical students, residents, and fellows in chronic noncancer pain. RESULTS: The database and hand-searches identified 545 articles; of these, 39 articles met inclusion criteria and underwent full review. Findings were classified into four inter-related themes. We found that managing chronic pain has been described as stressful by trainees, but few studies have investigated implications for their well-being or ability to provide empathetic care. Even fewer studies have investigated how educational strategies impact patient care. We also note that the literature generally focuses on opioids and gives less attention to education in nonpharmacological approaches as well as nonopioid medications. DISCUSSION: The findings highlight significant discrepancies between the prevalence of chronic pain in society and the low priority assigned to educating future physicians about the complexities of pain and the social context of those afflicted. This suggests the need for better pain education as well as attention to the "hidden curriculum."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".