The impact of anger and PTSD on marital satisfaction in canadian public safety personnel
Bibliographic record
Abstract
Canadian Public Safety Personnel (PSP) are regularly exposed to traumatic events in the line of their duties, making them more susceptible to posttraumatic stress disorder (PTSD; APA, 2013; Breslau, Chilcoat, Kessler, & Davis, 1999; Carleton et al., 2017). Examining risk and resilience factors that contribute to the development and maintenance of disorders such as PTSD in PSP is important for prevention and treatment . Anger and marital satisfaction have been identified as having major implications for the development, maintenance, and treatment of PTSD (Forbes et al., 2008; Meffert et al., 2008). In addition to anger being one of the characteristic symptoms of PTSD (APA, 2013), it is associated with increased risk of developing PTSD (Meffert et al., 2008). Marital satisfaction, on the other hand, is a protective factor against PTSD (Vest, Heavy, Homish, & Homish, 2017) and is also associated with lower levels of anger (Vest et al., 2017). This study examined the impact of anger and PTSD on marital satisfaction, while controlling for the confounding effects of depression. The sample included approximately 5813 PSP who participated in a large-scale online survey on mental disorders (Carleton et al., 2017). Symptoms of PTSD, anger, depression and martial satisfaction were assessed using self-report measures. Multiple hierarchical regression analyses identified higher anger (b = -.14) as a statistically significant predictor of lower marital satisfaction. The results suggest that targeting anger in PTSD screening and treatment may be helpful to improve marital relationship satisfaction, an important source of social support.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".