Family/household characteristics and positive mental health of Canadian military members: mediation through social support
Bibliographic record
Abstract
Introduction: The characteristics of one's social environment, including one's family or household composition, are recognized determinants of health and well-being. At the same time, a great deal of research has demonstrated the positive effect of social support on mental health in military populations. Methods: The aim of the current study was to provide a description of the various family/household characteristics of a sample of 6,696 CAF Regular Force members, including their marital status, living arrangement, and number of dependants, as well as the relationships of these characteristics with positive mental health (PMH). In addition, this study explored the role of social support as a possible mediating mechanism in these relationships. Results: Without accounting for levels of social support, it was found that service members who were married or in a common-law relationship demonstrated higher PMH, while those who were separated or divorced demonstrated lower PMH compared to their single counterparts. PMH also differed by living arrangement, with higher levels reported by service members living with others. Further analysis revealed that greater PMH reported by service members who were married or in a common-law relationship or who live with others could be attributed to their higher levels of social support. Discussion: Taken together, results emphasize the importance of social support as one of the mechanisms involved in the relationship between PMH and family/household composition. Given the limited research available on the combined effect of a variety of family/household factors, results of this work fill an important gap in the literature on the understanding of more complex relationships among these factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".