Equipping physicians for benzodiazepine receptor agonists deprescription in older adults: theory-based development of the BE-SAFE intervention
Bibliographic record
Abstract
BACKGROUND: Benzodiazepine receptor agonists (BZRA) are still widely used for sleep problems in older adults despite an unfavourable risk-benefit ratio. The hospital setting presents an opportunity for optimising medication use in older adults. The BE-SAFE project follows a rigorous sequential approach for developing a theory-informed intervention towards BZRA deprescription initiated in the hospital setting in 6 European countries (Belgium, Greece, Norway, Poland, Spain, and Switzerland). OBJECTIVES: The objectives of this paper are to describe the development of the physicians' intervention, the results of on the acceptability and perceived feasibility with physicians, and the finalised BE-SAFE intervention. METHODS: The intervention was built upon preliminary work and developed in four main steps: selection of behaviour change techniques; identification of existing resources and assessment of needs and preferences of hospital physicians; development of intervention components and modes of delivery; and end-users' evaluation and refinement of intervention. RESULTS: A total of 11 behaviour change techniques were selected, addressing 6 main barriers into a 6-component intervention: senior physician endorsement, training, self-monitoring, deprescription algorithm, communication to patients and other healthcare professionals. These core elements will be delivered allowing local adaptability. Twenty-four physicians evaluated the intervention. They confirmed that the intervention effectively targets the barriers highlighted in preliminary work and provided feedback for improvement regarding clarity and time issues. CONCLUSIONS: A 6-component physician's intervention to enhance BZRA deprescription initiated in hospital settings was developed by systematically addressing implementation issues in a theory informed manner. This intervention will be evaluated in a multi-country cluster randomised controlled trial.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".