Barriers to and Enablers of Supporting Deprescribing Benzodiazepines in Older Adults: A Survey of European Nonphysician Healthcare Professionals
Bibliographic record
Abstract
Although physicians are primarily responsible for Benzodiazepine Receptor Agonist (BZRA) deprescribing, nonphysician healthcare professionals (HCPs) can support deprescribing. This study explored barriers to and enablers of BZRA deprescribing among nonphysician HCPs. We surveyed 258 HCPs (63.2% nurses) working in hospital settings across six European countries using a questionnaire based on the Theoretical Domain Framework (TDF). Logistic regression assessed associations between TDF domains and both intentions to support and routine engagement in BZRA deprescribing. Major barriers (TDF items with mean < 3) were found in the goals (competing priorities), environmental context and resources (time and staff lack) and social influences (patient reluctance) domains. Five TDF domains were associated with a stronger intention to support deprescribing: social/professional role and identity (OR, 3.08; 95% CI, 1.77-5.46); beliefs about consequences (OR, 1.91; 95% CI, 1.07-3.34); memory, attention and decision processing (OR, 1.80; 95% CI, 1.16-2.82); intention to promote alternatives (OR, 1.63; 95% CI, 1.07-2.49); and reinforcement (OR, 1.57; 95% CI, 1.08-2.29). Knowledge was the only domain associated with routine BZRA deprescribing support (OR, 1.16; 95% CI, 1.06-1.27). Different categories of HCPs face similar major barriers, but barriers vary across HCP categories and countries. Context-specific, targeted interventions may enhance support for BZRA deprescribing.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".