Clinician Perspectives of Benzodiazepine Receptor Agonists (BZRA) Prescribing and Deprescribing in Ontario
Bibliographic record
Abstract
Background: BZRAs are commonly prescribed to older adults for the acute treatment of insomnia and anxiety, despite evidence-based recommendations cautioning against it due to an increased risk for falls/fractures, cognitive impairment and dependence, coupled with the added risk from inappropriate polypharmacy. Deprescribing BZRAs among older adults is challenging due to the many stakeholders involved (i.e., patient, clinicians, environment). Further, the prescribing/deprescribing landscape of BZRAs within smaller mixed urban-rural regions such as Southwestern Ontario has yet to be thoroughly investigated where access to resources and care coordination may differ. Objective: This study aims to explore clinician perspectives influencing BZRA prescribing/deprescribing practices within smaller mixed urban-rural regions in Ontario. Proposed Methods: This study employs a qualitative research design. Clinicians (primary care physicians, geriatricians, nurse practitioners) providing care to older adults within smaller mixed urban-rural regions in Ontario are invited to participate in a 30-45-minute semi-structured interview. Participants are asked about their rationale and perceived indications for prescribing BZRAs, the BZRA prescribing process and deprescribing efforts (including challenges/enablers). A directed content analysis using the Theoretical Domains Framework will be used for data analysis to identify emerging patterns and themes. Future applications: Findings may demonstrate a theoretical understanding of factors influencing BZRA prescribing/deprescribing practices among clinicians in smaller mixed urban-rural regions of Ontario. This study will provide a preliminary contextual understanding critical to developing BZRA prescribing initiatives that account for the health and social complexities within these understudied regions of Ontario.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".