Effects of Posttraumatic Stress Disorder and Age on the Association between Combat and Problematic Alcohol Use among Canadian Military Members Deployed in Support of the Mission in Afghanistan
Bibliographic record
Abstract
Alcohol abuse is associated with unique negative mental, physical, interpersonal, and occupational outcomes in the military. It is also often linked with traumatic combat exposure and posttraumatic stress disorder (PTSD) in an effort to relieve PTSD symptoms, in accordance with Conger’s (Citation1956) “self-medication hypothesis”. Furthermore, younger military personnel tend to be more likely than older members to abuse alcohol, which may be explained, at least in part, by Winick’s (Citation1962) “maturing out” hypothesis, which attributes the decline in alcohol abuse with age to the increased demands and responsibilities of adulthood roles, which do not accord with alcohol abuse. The current study investigated these theories simultaneously in a sample of Canadian military personnel (n = 15,832) surveyed 60 to 180 days upon return from deployment in support of the mission in Afghanistan. All unconditional effects were in the expected direction: greater combat exposure was significantly associated with greater PTSD symptomatology, which was in turn related to greater alcohol abuse. Alcohol abuse also decreased significantly in relation to increasing age. Additionally, a moderated mediation indicated that the association between combat exposure and alcohol abuse was partially mediated by PTSD symptoms, and this association was strongest among the youngest participants. These findings support both the self-medication and the maturing-out hypotheses of alcohol abuse, and highlight the importance of military education on effective coping strategies and awareness of available mental health resources, particularly among certain 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".