Barriers and enablers towards benzodiazepine-receptor agonists deprescribing in nursing homes: A qualitative study of stakeholder groups
Bibliographic record
Abstract
Background: Despite recommendations to deprescribe chronic benzodiazepine receptor agonists (BZRA) among older adults, the prevalence of their use in Belgian nursing homes (NHs) remains above 50%. The use of a behavioral science approach, starting with the evaluation of barriers and enablers for BZRA deprescribing, has the potential to decrease BZRA prescribing. Objectives: To identify barriers and enablers for BZRA deprescribing perceived by the different stakeholders involved in nursing home care in Belgium. Methods: In a purposive sample of 6 NHs, we conducted face-to-face interviews with general practitioners (GPs), and focus groups with other healthcare providers (HCPs), including nurses, pharmacists, occupational therapists, physical therapists, and with NH residents and relatives. All interviews with HCPs were analyzed through deductive thematic analysis, using the theoretical domains framework (TDF) as the coding framework. Residents' and relatives' interviews were analyzed using an inductive thematic approach. Results: We interviewed 13 GPs, 35 other HCPs, 22 nursing home residents, and 5 relatives. Overall, 9 TDF domains were identified as most relevant among HCPs interviewed: Skills, Beliefs about capabilities, Goals, Memory attention and decision processes, Environmental context and resources, Social influences, Knowledge, Social/professional role and identity, and Beliefs about consequences. Five additional themes emerged from residents' and relatives' interviews: knowledge on medications used, communication with NH staff and GPs, perceived efficacy and necessity of BZRA, influence of the environment, and reluctance towards BZRA deprescribing. Some domains and themes differ between stakeholders (e.g., knowledge), while others match between groups (e.g., environmental aspects). Conclusion: BZRA deprescribing is influenced by knowledge and skills gaps, automatic BZRA refilling, competing priorities, social challenges, environmental factors and poor nursing home residents involvement. Targeting these barriers will be a key step for implementation of BZRA deprescribing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".