L’information écrite des patients consommateurs chroniques de benzodiazépines pour favoriser l’arrêt : utilisation d’une brochure québécoise
Bibliographic record
Abstract
Background and purpose: the misuse and high prevalence of benzodiazepines use are a common problem in general medecine. Anglo-Saxon studies have shown effectiveness of a written intervention to promote their cessation. The objective of this study was to assess the effect of an information booklet on the prescription of benzodiazepines in french general medecine. Method: it was an interventional study, carried out with general practitioners in a french department around Poitiers between 2018 and 2019. They were divided in two groups. The intervention group was to distribute a booklet on benzodiazepines to consumer patients. The booklet used was from the Canadian study EMPOWER. The « control » group did not change their usual practice. The main outcome was the trend in the number of benzodiazepines-related acts. The secondary criteria were the overall and detailed amounts of bzd, and the feeling of the general practitioners about the intervention. The data came from the Health Insurance reimbursement databases. Results: 21 general practitioners were recruited to distribute the booklet, matched to 21 witnesses.No significant difference was found in the number of acts related to benzodiazepines after the intervention. The results were contradictory for the detail of the molecules. Feedback from participating physicians was positive regarding the provision of a written tool to facilitate the cessation. Conclusion: more longer-term studies are needed to develop and evaluate a tool to help doctors and patients limit the misuse of benzodiazepines.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".