Exploration of Barriers and Facilitators for Deprescribing Opioids and Benzodiazepines to Reduce Older Adult Falls
Bibliographic record
Abstract
Abstract As part of a randomized control trial for deprescribing opioids and benzodiazepines (BZD) to reduce falls (funded by Centers for Disease Control), we conducted a virtual focus group and surveys to evaluate opioid and BZD prescribing practices among healthcare providers in four primary care clinics in North Carolina. Survey and focus group questions measured providers’ confidence in their abilities to weigh benefits and harms of opioids and/or BZDs in older adults; determine alternative interventions; create a safe dosing plan; and incorporate patient preferences. A validated pre-intervention survey, adapted from a survey by the Canadian Deprescribing Network, was administered to providers in control and intervention clinics (n=29). Providers expressed high confidence in their abilities to weigh risks and benefits of deprescribing opioids and BZDs, but low confidence in deprescribing under impeding circumstances (e.g. when not the original prescriber or when there is no evidence to inform them). Results were similar across opioids and BZDs. A focus group was conducted among seven providers from the two intervention clinics. Barriers to deprescribing identified included patient resistance, lack of knowledge of deprescribing best practices, and lack of time to discuss deprescribing during regular clinic visits. Providers also expressed concerns about deprescribing medications initiated by or managed by other prescribers. Key facilitators of deprescribing included patient trust in physician, patients being agreeable to reduce medications, and use of gradual tapering rather than abrupt discontinuation. Barriers and facilitators were subsequently used to optimize training and provider resources for the deprescribing intervention, which is currently being implemented.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".