The impact of posttraumatic stress disorder on the psychological distress, positivity, and well-being of Australian police officers.
Bibliographic record
Abstract
OBJECTIVE: Police officers experience many traumatic events over the course of their career, often resulting in posttraumatic stress disorder (PTSD) and associated psychological distress. Studies have investigated the efficacy of interventions aimed at reducing symptoms of PTSD experienced by police officers, but lacking are studies investigating the impact of PTSD on positivity, a construct we define as a latent variable estimated using self-report measures of optimism, gratitude, self-compassion, and mindfulness. The present study carried out a path analysis of a model testing the hypothesis that PTSD would be associated with increased psychological distress and decreased positivity, both of which influence well-being. The model also evaluated associations between constructs that could be modified through interventions to increase well-being-associations between posttraumatic growth, social support, physical activity and psychological distress, positivity, and well-being. METHOD: = 506) completed an online survey that included self-report measures of the constructs included in the model being tested. RESULTS: ² = .79. Results found that neither PTSD or psychological distress had a direct effect on well-being. Psychological distress indirectly influenced well-being by lowering levels of positivity, while positivity was associated with higher scores on the measure of well-being. CONCLUSIONS: The implication of the results is that interventions aimed at enhancing positivity could be expected to improve well-being in police officers and offering traditional therapies together with positivity enhancing therapies may have additional benefits over either alone. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".