Deprescribing benzodiazepine receptor agonists in older adults: a mixed-methods study to adapt the Canadian D-PRESCRIBE intervention to the Belgian community setting
Bibliographic record
Abstract
OBJECTIVE: Guidelines recommend deprescribing benzodiazepine receptor agonists (BZRA) in older adults, yet implementation in clinical practice remains limited. Adapting effective, evidence-based interventions to a new context is a resource-saving strategy. In Canada, the D-PRESCRIBE intervention comprised a patient educational brochure and a pharmaceutical opinion inviting physicians to revise BZRA prescribing and consider safer alternatives. Due to its effectiveness on BZRA deprescribing among Canadian older adults, we aimed to adapt the D-PRESCRIBE intervention to the Belgian community setting. DESIGN: Recommendations from the ADAPT guidance, that provides a systematic approach for adapting interventions to new contexts, were followed. We conducted a mixed-methods study that comprised (1) group discussions and cognitive interviews to assess the acceptability and need for adaptation of the intervention's components and (2) a survey on the adapted pharmaceutical opinion. A research committee involving stakeholders' representatives decided on the adaptations, respecting the core functions of both tools. Changes in intervention components were reported following the Model for Adaptation Design and Impact framework. SETTING: Belgian French-speaking community setting. PARTICIPANTS: Six older adults (≥65 years), six general practitioners (GPs) and seven pharmacists participated in the group discussions or interviews. 46 GPs and 91 pharmacists responded to the survey. RESULTS: Participants welcomed the brochure positively. Still, some changes in the vocabulary, wording, photos and icons were made for several purposes including making the patient feel concerned about the brochure and softening the use of fear. The pharmaceutical opinion aroused mixed perceptions. Its name, layout and content were adapted to enhance its acceptability and fit with our healthcare system, practices and national guidelines. The survey highlighted several enablers and barriers to its use from the perspectives of GP and pharmacist. CONCLUSIONS: The Canadian D-PRESCRIBE intervention was adapted to the Belgian setting following a thorough and transparent process. Its feasibility will be tested in a future pilot study (NCT:05929417).
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".