General surgeons’ perspectives on post-discharge opioid prescribing: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Understanding surgeons' perspectives on post-discharge opioid prescribing is crucial for optimizing pain management while mitigating opioid-related harms. OBJECTIVE: To describe the perspectives of North American general surgeons towards opioid prescribing after hospital discharge. METHODS: This qualitative study involved semi-structured interviews with 30 general surgeons in the USA and Canada. Interviews were audio-recorded, transcribed verbatim, and continued until thematic saturation. Data were analyzed using inductive thematic analysis. RESULTS: Three themes were derived: motives for relying on opioids, motives for opioid minimization, and strategies for tailoring analgesia. Reliance on opioids was motivated by prescribing culture, convenience, patients' expectations, apprehension towards non-opioid analgesics, and limited pain management expertise. Motivations for opioid-minimization included cultural shift, non-opioids' effectiveness, policy, and emerging research. Strategies for tailoring prescribing included addressing patient expectations and post-discharge follow-up. CONCLUSIONS: Barriers to evidence-based prescribing, including tradition, convenience, and lacking expertise, should be addressed to optimize analgesia and mitigate opioid-related harms.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".