Measuring positive mental health in Canada: construct validation of the Mental Health Continuum—Short Form
Bibliographic record
Abstract
INTRODUCTION: Positive mental health is increasingly recognized as an important focus for public health policies and programs. In Canada, the Mental Health Continuum-Short Form (MHC-SF) was identified as a promising measure to include on population surveys to measure positive mental health. It proposes to measure a three-factor model of positive mental health including emotional, social and psychological well-being. The purpose of this study was to examine whether the MHC-SF is an adequate measure of positive mental health for Canadian adults. METHODS: We conducted confirmatory factor analysis (CFA) using data from the 2012 Canadian Community Health Survey (CCHS)-Mental Health Component (CCHS-MH), and cross-validated the model using data from the CCHS 2011-2012 annual cycle. We examined criterion-related validity through correlations of MHC-SF subscale scores with positively and negatively associated concepts (e.g. life satisfaction and psychological distress, respectively). RESULTS: We confirmed the validity of the three-factor model of emotional, social and psychological well-being through CFA on two independent samples, once four correlated errors between items on the social well-being scale were added. We observed significant correlations in the anticipated direction between emotional, psychological and social well-being scores and related concepts. Cronbach's alpha for both emotional and psychological well-being subscales was 0.82; for social well-being it was 0.77. CONCLUSION: Our study suggests that the MHC-SF measures a three-factor model of positive mental health in the Canadian population. However, caution is warranted when using the social well-being scale, which did not function as well as the other factors, as evidenced by the need to add several correlated error terms to obtain adequate model fit, a higher level of missing data on these questions and weaker correlations with related constructs. Social well-being is important in a comprehensive measure of positive mental health, and further research is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".