Problematic anger in a treatment‐seeking Canadian veteran population: Prevalence, assessment, and treatment implications
Bibliographic record
Abstract
Abstract Anger is a natural, adaptive emotion that is culturally accepted in military settings. Problematic anger (i.e., intense anger paired with significant distress and functional impairment linked to) is gaining attention in military and veteran populations. This study examined problematic anger in 882 Canadian Armed Forces personnel and veterans referred to a specialized mental health clinic. Intake assessments included measures of anger, psychological conditions, and military and demographic variables. Approximately 63% of participants reported problematic anger. Respondents who endorsed problematic anger had higher rates of posttraumatic stress disorder (PTSD) symptom severity, d = 1.06; depression, d = 0.82; anxiety symptom frequency, d = 0.94; and harmful drinking, d = 0.36, ps < .001, compared to those who did not. Sequential linear regression analyses demonstrated that PTSD symptom severity, Badjusted = 0.18, 95% CI [0.16, 0.20], R2 = .37, and anxiety symptom frequency, Badjusted = 0.55, 95% CI [0.49, 0.61], R2 = .33, accounted for the largest proportion of the variance in problematic anger symptom severity. This is the first study to report on problematic anger in a Canadian military/veteran context, and the results suggest that almost two thirds of veterans endorsed problematic anger, which is higher than previously reported prevalence rates. This study is a starting point for better understanding risk and vulnerability factors for problematic anger among Canadian military personnel and veterans and clarifying the associations among problematic anger, PTSD, and anxiety symptoms. Implementing standardized screening for problematic anger may improve diagnostic precision, treatment planning, and outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".