Barriers to discontinuing benzodiazepine receptor agonists in older adults: a survey of older adults across Europe
Bibliographic record
Abstract
BACKGROUND: Various factors hinder older adults from discontinuing benzodiazepine receptor agonists (BZRAs). Identifying and prioritising these barriers is essential for designing effective interventions to discontinue BZRAs. OBJECTIVE: To identify barriers to BZRA discontinuation among older adults and factors associated with their willingness to reduce or stop use. METHODS: A cross-sectional survey was conducted among adults 65+ using BZRA for sleep problems, recruited from hospitals across six European countries. Barriers were identified via a 27-item questionnaire grounded in the Theoretical Domains Framework (TDF), which systematically identifies individual and contextual determinants of behaviour. Responses were analysed using descriptive statistics. Multivariable logistic regressions identified factors associated with patients' willingness to reduce or stop BZRA. RESULTS: Among 183 participants, 59.1% were willing to reduce and 42.7% to stop BZRA if recommended by their doctor. Half understood why discontinuation is necessary. Barriers were present for most participants across multiple TDF domains. They included: high satisfaction with BZRA, perceived low risk of side effects, limited coping skills or ability to stop, fear of discontinuation and lack of support from physicians or social networks. Higher scores in the TDF domains of Goals, Emotion and Social Influences were associated with greater willingness to reduce BZRA. These domains and Reinforcement, Environmental context and resources were also linked to a greater willingness to stop. CONCLUSIONS: These findings highlight the opportunities and challenges of discontinuing BZRA in older adults. While half know the need to discontinue and are willing to try, future interventions must address pervasive barriers across many behavioural domains.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".