The association between length of benzodiazepine use and sleep quality in older population
Bibliographic record
Abstract
BACKGROUND: Sleep disturbances are frequently reported in the older adult population and benzodiazepines are the drugs most often prescribed to treat these problems. Nearly 25% of the older adult population uses these drugs and 83% of benzodiazepine users report sleep problems. Although the Collège des Médecins du Québec suggests a maximum length of use of 3 months, according to most studies the mean length of benzodiazepine use is longer. The goal of this study was to document the association between length of benzodiazepine use and sleep quality as reported by adults 65 years older and over. METHODS: Data used in this study came from the Seniors' Health Survey (ESA) conducted in a representative sample of the community-dwelling older population in Quebec, Canada. Inclusion criteria included the ability to speak and understand French. Data were analyzed using a structural equation modeling strategy. RESULTS: Long-term benzodiazepine users were more likely to report poor sleep quality. Sleep quality of initial probable problematic sleepers tended to increase over 1 year but sleep quality in benzodiazepines users increased less rapidly than in non-users. Also, women were more likely to report using benzodiazepines and having poorer sleep quality. CONCLUSION: Longitudinal studies using incident cases of benzodiazepine use should be conducted to better determine the causal relationship between sleep quality and benzodiazepine use in the older 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".