Impact benzodiazepine dependence on the use of health services: senior’ health study
Bibliographic record
Abstract
CONTEXT: Prolonged use of benzodiazepines increases the risk of addiction. The impact of this disease on the use of health services by older adults is not known. OBJECTIVE: Examine the association between benzodiazepine dependence and use of health services by older adults in Quebec. Methodology. The data comes from a survey conducted in Quebec in 2005-2006 with a representative sample of 707 elderly benzodiazepine consumption in the community. Benzodiazepine dependence was defined according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Revised Edition. The use of health services as measured by the incidence of consultations with health professionals over a period of 12 months. RESULTS: Seniors have consumed an average daily dose of 6.1 (± 7.6) mg diazepam equivalent to an average of 205 (± 130) days. The prevalence of benzodiazepine dependence has been estimated at 9.5%. This dependence increases the likelihood of consulting a specialist (odds ratio (OR) = 3.42; confidence interval 95% (CI 95%) = 1.38 to 8.50). Visits to other health professionals frontline were not significant. CONCLUSION: The results of this study suggest that the proportion of seniors who become addicted to benzodiazepines and do not use health services for this condition is important. There is a need to develop programs to improve the quality of benzodiazepine use in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".