Risk Factors for Snoring in Two Canadian First Nations Communities
Bibliographic record
Abstract
Snoring may be an important predictor of sleep-disordered breathing. Factors related to snoring among First Nations people are not well understood in a population with high rates of smoking and excess body weight. An interviewer-administered survey was conducted among 874 individual participants from 406 households in 2012 and 2013 in two Canadian First Nations communities. The survey collected information on demographic variables, individual and contextual determinants of respiratory health and snoring (classified as present versus absent) and self-reported height and weight. Multiple logistic regression analyses were conducted to examine relationships between snoring and potential risk factors adjusting for age and sex. Snoring was present in 46.2% men and 47.0% women. Considering body mass index, 259 people (30.3%) were overweight and 311 (36.4%) were considered obese. The combined current/former smoking rate was 90.2%. Being overweight, obesity, sinus trouble, current smoking status and former smoking were significantly associated with snoring. Exposure to home dampness and mold were suggestive of an association with snoring. To the degree that snoring may be a predictor of possible sleep-disordered breathing, these results indicate that environmental conditions such as smoking and home exposures may be important factors in the pathogenesis of these conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".