Provider knowledge, beliefs, and self‐efficacy to deprescribe opioids and sedative‐hypnotics
Bibliographic record
Abstract
BACKGROUND: While many studies have assessed and measured patient attitudes toward deprescribing, less quantitative research has addressed the provider perspective. We thus sought to describe provider knowledge, beliefs, and self-efficacy to deprescribe, with a focus on opioids and sedative-hypnotics. METHODS: An electronic anonymous survey was distributed to primary care providers at Kaiser Permanente Washington. Two reminder emails were sent. The survey included 10 questions on general deprescribing, and six questions each specific to opioid and sedative-hypnotic deprescribing. Knowledge questions used a multiple-choice response option format. Questions addressing beliefs and self-efficacy (i.e., confidence) used a 0-10 Likert scale. Scales were dichotomized at ≥7 to define agreement (belief questions) or confidence (self-efficacy questions). We calculated descriptive statistics to summarize the responses. RESULTS: Of 370 eligible primary care providers, 95 (26%) completed the survey. For general deprescribing questions, a majority believed that lack of patient willingness, withdrawal symptoms and fear of symptom return, and time constraints impeded deprescribing. Approximately half chose the correct answers about opioid deprescribing, 21% were confident that they could alleviate patient concerns about opioid tapering, and 32% were confident managing chronic non-cancer pain without opioids. For sedative-hypnotics, 64%-87% of respondents correctly answered questions about risks and the relative effectiveness of alternatives, but only one-third correctly answered a question about sedative-hypnotic tapering. Roughly half were confident in their ability to successfully engage patients in sedative deprescribing conversations and select alternatives. Only 54% and 34% were confident in writing a tapering protocol for opioids and sedative-hypnotics, respectively. CONCLUSION: Results suggest that raising provider awareness of patient willingness to deprescribe, addressing knowledge gaps, and increasing self-efficacy for deprescribing are important targets for improving deprescribing. Support for writing tapering protocols and prescribing evidence-based drug and non-drug alternatives may be important to improve care.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".