Sedative Medication Use: Prevalence, Risk Factors, and Associations with Body Mass Index Using Population-Level Data
Bibliographic record
Abstract
STUDY OBJECTIVES: To estimate the prevalence of and identify sociodemographic risk factors for sedative medication use in the general Canadian population, and to examine the association between sedative medication use and body mass index (BMI). DESIGN: Cross-sectional study SETTING: Canadian population PARTICIPANTS: Participants from the 1994-2003 Canadian national health surveys, the National Population Health Survey (NPHS) and the Canadian Community Health Survey (CCHS). For the 2003 CCHS, n = 134,072, ages 12-80+ years. INTERVENTIONS: Not applicable MEASUREMENTS AND RESULTS: The overall prevalence of sedative medication use in Canada in 2003 was 5.5%, having more than doubled since 1994. Notable rises in sedative medication use have occurred among men, non-elderly, and obese individuals. After adjusting for potential sociodemographic and health status confounders, including psychiatric comorbidities, the odds of sedative use were significantly greater among morbidly obese (BMI ≥ 35 kg/m(2)) men (OR = 1.89, 95%CI = 1.02-3.53) and underweight (BMI < 18.5 kg/m(2)) women (OR = 2.11, 95%CI = 1.26-3.53). CONCLUSIONS: The use of sedative medications has substantially risen among the general Canadian population, and among particular population subgroups. The greater odds of sedative medication use found among morbidly obese men may reflect the presence of underlying obstructive sleep apnea, which may in turn serve to explain in part the known relationship between sedative medications and mortality. The increase in sedative medications coupled with their known adverse health associations raises potential public health concerns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".