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Record W4410552054 · doi:10.1016/j.sapharm.2025.05.002

Equipping physicians for benzodiazepine receptor agonists deprescription in older adults: theory-based development of the BE-SAFE intervention

2025· article· en· W4410552054 on OpenAlexaff
François‐Xavier Sibille, Joanna Salbert, Lucy Bolt, Vagioula Tsoutsi, Enrico Callegari, Olivia Dalleur, Tokandji Adda, Thomas Agoritsas, Thomas Berger, Carole E. Aubert, Dimitris Dikeos, Antoni Salvà, B Sanchez Pascual, Ramón Miralles, Torgeir Bruun Wyller, Andrea M. Patey, Jeremy Grimshaw, Adam Wichniak, Anne Spinewine, Marie de Saint Hubert

Bibliographic record

VenueResearch in Social and Administrative Pharmacy · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsOttawa Hospital
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungFonds De La Recherche Scientifique - FNRSStaatssekretariat für Bildung, Forschung und InnovationOffice Fédéral de l'Education et de la ScienceHORIZON EUROPE HealthSpine Education and Research Institute
KeywordsBenzodiazepineIntervention (counseling)MedicinePsychologyPharmacologyReceptorPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.150
GPT teacher head0.497
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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