The Prevalence and the Associated Factors of Anxiety Among First Nations Populations of Saskatchewan
Bibliographic record
Abstract
Background: Sleep disturbances effect how the brain functions and can therefore contribute to anxiety. Some anxiety disorder descriptions, including generalized anxiety disorder and posttraumatic stress disorder, have integrated sleep conditions into their diagnostic criteria, such as insomnia and nightmares. Although anxiety is recognized as a common mental illness, knowledge about its prevalence and associated factors (specifically sleep-related variables) is limited for First Nations populations in Canada. Purpose: To determine the prevalence of anxiety among two First Nations communities and associated factors. Methods: Data from the First Nations Sleep Health Project (FNSHP) was collected between 2018 and 2019. All community participants (18 years and older) were invited by trained research assistants to participate in FNSHP. Completed questionnaires were obtained from 588 participants (260 males, and 328 females). The questionnaire collected information on individual factors (diagnoses such as anxiety, chronic pain, and health behaviors), contextual factors (household characteristics such as income and education), and covariates (age and sex). Logistic regression was the primary method of analysis. Results: The overall prevalence of anxiety was 32% and greater among females (35.7%) than males (28.0%). Sleep quality was associated with anxiety after adjusting for other variables. Other variables that were statistically significantly associated with higher odds of anxiety were chronic bronchitis, post-traumatic stress disorder (PTSD), and depression. In addition, the relationship between sleep quality and anxiety was modified by the presence of household smoke. Conclusion: Further research based on a larger sample and preferably a longitudinal study design is needed to examine associated factors for anxiety in First Nations populations of Saskatchewan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".