Association between social support and mental health conditions in treatment-seeking Veterans and Canadian Armed Forces personnel
Bibliographic record
Abstract
Introduction: Despite limited research on the topic, it has been observed that military members face unique challenges with social support. Methods: The current study used data provided by treatment-seeking Veterans and Canadian Armed Forces (CAF) members ( N=666) to: (1) determine whether symptomatology of posttraumatic stress disorder (PTSD), depression (MDD), anxiety, and suicidal ideation (SI) increased as level of perceived social support decreased; and (2) identify if the level of perceived social support is associated with PTSD, MDD, and anxiety symptom distress and SI frequency; this was done while controlling for demographic factors. Social support was measured using a single item grouped according to “low,” “medium,” and “high” levels of perceived support. Results: Overall, adequate social support was low with less than one-third (29%) of participants reporting a high level. There was an inverse association between social support and symptom distress for all mental health conditions, whereby those who perceived low social support had significantly greater symptom distress than those who perceived medium social support, who in turn reported significantly greater symptom distress than those perceiving high social support. Social support was significantly associated with all mental health conditions when controlling for demographic variables. The effect of social support on PTSD and SI affected Veterans and CAF members differently. Discussion: Our study highlights the difficulty this population faces in maintaining adequate social support alongside military-related mental health disorders. More research is required to fully understand the role of social support in military populations.
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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 | 0.002 | 0.002 |
| 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) | 0.003 | 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".