Brief Intervention for Discontinuing Inappropriate Z-Hypnotic Use Among Older Patients in Primary Care: Protocol for a Cluster Randomized Controlled Trial With a Single Crossover
Bibliographic record
Abstract
BACKGROUND: Older patients are frequent users of Z-hypnotics despite consensus recommendations against extended use. Inappropriate Z-hypnotic use among older patients is frequently reported, posing risks of side effects and dependence. Interventions have been mainly at the population level and through prescription regulations. There are few instruments helping general practitioners (GPs) deal with inappropriate use among individual patients. OBJECTIVE: Through a randomized controlled trial (RCT), we aim to test the effectiveness of a behavioral brief intervention (BI) method used by trained GPs for reducing inappropriate Z-hypnotic use among their patients. METHODS: We will conduct a double-blind RCT with a single crossover. Patients (aged >60 years) on participating GPs' lists who are using Z-hypnotics inappropriately, do not have serious mental or physical disorders, and can provide valid informed consent are eligible. GPs randomized to the BI arm will be trained to administer the BI, and those randomized to business as usual (BAU) will not receive training. GPs' patient lists will be screened for inappropriate Z-hypnotic users through an electronic questionnaire. The GP will be informed of patients who should be given an appointment and administered the BI. Untrained GPs will continue BAU. Randomization-blinded outcome evaluation will be conducted at 6 weeks, 6 months, and 1 year in both the study groups. RESULTS: The main outcome is the proportion of patients with inappropriate Z-hypnotic use, comparing BI versus BAU, after 6 weeks. Secondary outcomes are cognitive function, pain, self-reported sleep evaluation, sleep efficiency (actigraphy) and quality of life, and change compared to baseline. We will also report on the characteristics of the screened GP patient population. Other variables are other medication use or polypharmacy, anxiety and depression, severity of dependence, and mortality. CONCLUSIONS: If RCT-level evidence demonstrates the effectiveness of the BI for reducing inappropriate Z-hypnotic use among older patients without worsening of secondary outcomes, this could be a simple, transferable intervention to implement on a larger scale among GPs, other physicians, and health workers. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75670.
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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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
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".