Brief interventions targeting long‐term benzodiazepine and Z‐drug use in primary care: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: To assess the effectiveness of brief interventions in primary care aimed at reducing or discontinuing long-term benzodiazepine/Z-drug (BZRA) use. METHOD: Systematic review of randomized controlled trials of brief interventions in primary care settings aimed at reducing or discontinuing long-term BZRA use in adults taking BZRAs for ≥ 3 months. Four electronic databases were searched: PubMed, EMBASE, PsycINFO and CENTRAL. The primary outcome was BZRA use, classified as discontinuation or reduction by ≥ 25%. The Theoretical Domains Framework (TDF) was used to retrospectively code behavioural determinants targeted by the interventions. The Behaviour Change Technique (BCT) Taxonomy was used to identify the interventions' active components. Study-specific estimates were pooled, where appropriate, to yield summary risk ratios (RRs) and 95% confidence intervals (CIs). Pearson's correlations were used to determine the relationship between intervention effect size and the results of both the TDF and BCT coding. RESULTS: Eight studies were included (n = 2071 patients). Compared with usual care, intervention patients were more likely to have discontinued BZRA use at 6 months (eight studies, RR = 2.73, 95% CI = 1.84-4.06) and 12 months post-intervention (two studies, RR = 3.41, 95% CI = 2.22-5.25). TDF domains 'knowledge', 'memory, attention and decision processes', 'environmental context and resources' and 'social influences' were identified as having been included in every intervention. Commonly identified BCTs included 'information about health consequences', 'credible source' and 'adding objects to the environment'. There was no detectable relationship between effect size and the results of either the TDF or BCT coding. CONCLUSION: Brief interventions delivered in primary care are more effective than usual care in reducing and discontinuing long-term benzodiazepine/Z-drug use.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".