Barriers to Supporting Deprescribing Benzodiazepines in Older Adults: A Survey of European Non-Physician Healthcare Professionals
Bibliographic record
Abstract
While physicians are primarily responsible for Benzodiazepine Receptor Agonist (BZRA) deprescribing, non-physician healthcare professionals (HCPs) can support deprescribing. This study explored barriers to BZRA deprescribing faced by non-physician HCPs. We surveyed 258 HCPs (63.2% nurses) across six European countries using a Theoretical Domain Framework (TDF)-based questionnaire. Logistic regression assessed associations between TDF domains and HCPs’ intention to support deprescribing and their routine support of BZRA deprescribing. Major barriers (defined as TDF items with a mean<3) were found in the Goals (competing priorities), Environmental Context and Resources (time and staff lack), and Social Influence (patient reluctance) domains. Five TDF domains were associated with a stronger intention to support deprescribing: Social/Professional Role and Identity (odds ratio [OR], 3.08; 95% confidence interval [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. Adapted to contextual differences, targeted interventions may address barriers, enhancing 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".