Correlates of well-being among Canadians with mood and/oranxiety disorders
Bibliographic record
Abstract
INTRODUCTION: Our objective was to examine variables associated with well-being as measured by high self-rated mental health (SRMH) and life satisfaction (LS), among Canadian adults (aged 18+) living with a mood and/or an anxiety disorder. METHODS: We used nationally representative data from the 2014 Survey on Living with Chronic Diseases in Canada-Mood and Anxiety Disorders Component (SLCDC-MA) to describe the association between well-being and self-management behaviours (physical activity, sleep and meditation) as well as perceived stress, coping and social support. We used multivariate logistic regression to model the relationship between these factors and measures of well-being. RESULTS: Approximately one in three individuals with mood and/or anxiety disorders reported high SRMH. The logistic regression models demonstrated that several characteristics such as being older, and reporting higher self-rated general health, fewer functional limitations, lower levels of perceived life stress, higher levels of perceived coping and higher levels of perceived social support were associated with higher levels of wellbeing. Self-management behaviours (including starting physical activity, meditation, adopting good sleep habits and attaining a certain number of hours of nightly sleep) were not significantly associated with measures of well-being in our multivariate model. CONCLUSION: Canadian adults with mood and/or anxiety disorders who reported lower levels of perceived stress and higher levels of social support and coping were more likely to report high levels of well-being. This study contributes evidence from a representative population-based sample indicating well-being is achievable, even in the presence of a mood and/or an anxiety disorder.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".