Psychometric Evaluation of the Mental Health Continuum–Short Form in French Canadian Young Adults
Bibliographic record
Abstract
OBJECTIVE: To examine the factor structure, internal consistency, reliability, sex invariance, and discriminant validity of the French Canadian version of the Mental Health Continuum-Short Form (MHC-SF). METHOD: A total of 1485 French-speaking postsecondary students in Quebec, Canada (58% female; mean age = 18.4, SD = 2.4), completed the MHC-SF. Confirmatory factor analysis (CFA) was used to assess the factorial structure of the MHC-SF. Internal consistency was assessed with Cronbach's alpha, and reliability was assessed with the rho reliability coefficient. Invariance testing across sex was conducted using multigroup CFA comparing 4 increasingly restrictive models, and discriminant validity was examined against the Hospital Anxiety and Depression Scale (HADS) using Pearson correlation coefficients and CFA. RESULTS: CFA supported the correlated 3-factor structure of the MHC-SF, with emotional, social, and psychological well-being subscales. The scale and each subscale items had internal consistency coefficients (Cronbach's alphas) above .70 and reliability coefficients (Jöreskog's rho) ranging from .79 to .90. Based on the multigroup CFA, configural, metric, scalar, and error variance invariance of the MHC-SF was observed across sex. Finally, the 2-continua model, suggesting that mental health and mental illness are distinct but related dimensions, was supported by both moderate inverse correlations between MHC-SF and HADS subscale scores and the 2-factor structure in CFA. CONCLUSIONS: These data support the multidimensional structure of the MHC-SF and provide evidence of internal consistency, reliability, and invariance across sex. The MHC-SF is a valid and reliable measure of mental health that is distinct from mental illness among French Canadian young adults.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".