Typology of sleep medication users and associated mental health and substance use from a Montreal epidemiological study.
Bibliographic record
Abstract
BACKGROUND: Sleep medication is often reported as one of the most highly used psychotropic drugs in terms of past-year prevalence. Since their use often varies according to the characteristics of individuals, it is important to better understand these particular utilization patterns. OBJECTIVES: The study aims to develop a typology of sleep medication users' characteristics, including their associated mental health and substance use. METHODS: Residents from the epidemiological area of south-west Montreal, Quebec aged 15 years and older responded to a questionnaire in 2009 and 2011. Among the 1822 people who participated at both T1 and T2, 306 (17%) reported use of medication to help them sleep. These participants were selected for cluster analysis based on five variables related to mental health. The identified clusters were then tested for association with sociodemographic, psychosocial, and service use characteristics. RESULTS: A three-cluster solution emerged: 1) older individuals without mental health problems, drug use or psychotropic medication use; 2) individuals with elevated psychological distress, drug use and low social support, and 3) individuals with mood and anxiety disorders, using services for mental health and taking two or more psychotropic medications. CONCLUSIONS: The results establish the significance of problems related to mental health in differentiating sleep medication users. Consideration of these differences may improve the ability of health professionals to provide services that are better suited for patients, including interventions that increase the ability to cope with stress (cluster 2) and more integrated services for those with concurrent disorders (cluster 3).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".