The fundamental association between mental health and life satisfaction: results from successive waves of a Canadian national survey
Bibliographic record
Abstract
BACKGROUND: A self-reported life satisfaction question is routinely used as an indicator of societal well-being. Several studies support that mental illness is an important determinant for life satisfaction and improvement of mental healthcare access therefore could have beneficial effects on a population's life satisfaction. However, only a few studies report the relationship between subjective mental health and life satisfaction. Subjective mental health is a broader concept than the presence or absence of psychopathology. In this study, we examine the strength of the association between a self-reported mental health question and self-reported life satisfaction, taking into account other relevant factors. METHODS: We conducted this analysis using successive waves of the Canadian Community Health Survey (CCHS) collected between 2003 and 2012. Respondents included more than 400,000 participants aged 12 and over. We extracted information on self-reported mental health, socio-demographic and other factors and examined correlation with self-reported life satisfaction using a proportional ordered logistic regression. RESULTS: Life satisfaction was strongly associated with self-reported mental health, even after simultaneously considering factors such as income, general health, and gender. The poor-self-reported mental health group had a particularly low life satisfaction. In the fair-self-reported mental health category, the odds of having a higher life satisfaction were 2.35 (95% CI 2.21 to 2.50) times higher than the odds in the poor category. In contrast, for the "between 60,000 CAD and 79,999 CAD" household income category, the odds of having a higher life satisfaction were only 1.96 (95% CI 1.90 to 2.01) times higher than the odds in the "less than 19,999 CAD" category. CONCLUSIONS: Subjective mental health contributes highly to life satisfaction, being more strongly associated than other selected previously known factors. Future studies could be useful to deepen our understanding of the interplay between subjective mental health, mental illness and life satisfaction. This may be beneficial for developing public health policies that optimize mental health promotion, illness prevention and treatment of mental disorders to enhance life satisfaction in the general population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.002 | 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".