Gender/sex disparity in self-reported sleep quality among Canadian adults
Bibliographic record
Abstract
OBJECTIVE: This study investigated gender differences in sleep quality among Canadian adults in a population-representative survey. METHODS: Data for this study was provided by the Canadian Community Health Survey (CCHS). For respondents (n = 39,700) who completed the 2011-12 CCHS sleep module, multinomial logistic regression investigated the relationship between gender and a composite sleep quality measure among adults ³18 years old, adjusted for confounders. RESULTS: Among the sample, gender was evenly distributed (49.3% men, 50.7% women). In the adjusted logistic model, female gender was independently associated with higher odds of poor sleep quality at all levels of poor sleep quality (from ‘a little of the time’ AOR=1.47, 95%CI:1.24, 1.73 to ‘all of the time’ AOR=2.10, 95%CI:1.74, 2.54). This disparity was progressively greater the more frequent the poor sleep quality reported for all but the highest poor sleep quality level. CONCLUSIONS: This study provides population-level evidence of a sleep quality disparity for Canadian women. Using a mixed gender population-based sample and a robust composite sleep quality measure, this study contributes to a growing understanding of poor sleep as a population health issue. Further research is needed to understand the mechanisms underlying this relationship, as well as to investigate effective public health and policy interventions for addressing sleep-gender population health disparities.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".