Validation of the mental health continuum: Short form among Canadian Armed Forces personnel
Bibliographic record
Abstract
Background: Compared to the general Canadian population, military members exhibit a higher prevalence of depressive disorders, anxiety disorders, and post-traumatic stress disorder. However, there is a paucity of research investigating the extent to which military members experience positive mental health. Validation of positive mental health measures, including the Mental Health Continuum - Short Form (MHC-SF), is necessary to determine whether well-being can be assessed in a valid and reliable manner among Canadian Armed Forces (CAF) military members. The purpose of this research was to assess the internal consistency reliability, convergent validity, factor structure, and measurement invariance of the MHC-SF among CAF Regular Force and Reserve Force military members. Data and methods: Data were drawn from the nationally representative 2013 Canadian Forces Mental Health Survey (CFMHS) conducted by Statistics Canada. A random sample of 8,200 CAF military personnel completed the CFMHS, representing 64,400 Regular Force and 4,460 Reserve Force CAF personnel. Results: As expected, all three MHC-SF subscales (psychological, social, and emotional well-being) correlated positively with life satisfaction, self-rated mental health, sense of belonging, and social support, and correlated negatively with psychological distress and disability due to health conditions. Internal consistency was high. Confirmatory factor analysis supported the three-factor structure of the MHC-SF, and measurement invariance was satisfied. Interpretation: Findings provided support for the reliability, convergent validity, factorial validity, and measurement invariance of the MHC-SF among both Regular Force and Reserve Force military samples. Therefore, researchers and clinicians can reliably implement the MHC-SF as a tool to assess, interpret, and predict military members' psychological, social, and emotional well-being.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".