Opioid Education and Prescribing Practices
Bibliographic record
Abstract
BACKGROUND: Lack of training among health care providers (HCPs) to safely prescribe opioids is a contributing factor to the opioid crisis. Training and other resources have been developed to educate providers about safe and appropriate opioid prescribing practices. METHOD: The national survey was conducted with 2000 HCPs representing primary care physicians (PCPs), including family practice, general practice, and internal medicine; specialists (SPs); physician assistants (PAs); and nurse practitioners (NPs), a mix of primary care and specialists. This survey examined exposure to opioid educational information and opioid prescribing. RESULTS: PCPs reported prescribing opioids for chronic pain to significantly more patients compared with other HCP groups. PCPs (89.8%) and NPs (85.5%) reported significantly greater exposure to opioid educational information compared with both SPs (71.9%) and PAs (78.8%). Overall, HCPs had limited knowledge about abuse-deterrent formulations, but PCPs had greater knowledge than other groups. HCPs had an increased likelihood of prescribing opioids to fewer patients in the last 3 months relative to the prior 12 months if they worked in a state or county clinic vs a solo or group practice type (adjusted odds ratio [AOR] = 1.97; 95% confidence interval [CI], 1.12-3.49) and were exposed to more opioid educational information during the last 12 months (AOR = 1.19; 95% CI, 1.06-1.32). DISCUSSION: HCPs' exposure to opioid educational information was associated with less opioid prescribing for chronic pain. Findings indicated a difference in exposure and knowledge gaps across provider groups. More information is needed on the content of opioid educational information provided to HCPs.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".