Resilience among police officers: the role of personality functioning and protective factors
Bibliographic record
Abstract
Purpose Police officers face a unique and challenging occupational experience and report elevated mental disorder symptoms relative to the general population. While gender differences appear to be present in police mental health, this study aims to find which factors foster and promote resilience in these workers and how gender may relate to police resilience. Design/methodology/approach The present study was designed to explore how protective factors, sexual harassment and personality dysfunction impacted resilience among police officers (n = 380; 44% women). Furthermore, gender differences were also examined on these factors as well as on resilience rate. Findings Men and women police officers did not differ significantly in terms of resilience, protective factors and overall experiences of sexual harassment behaviors; yet, policewomen subjectively reported having experienced more sexual harassment in the past 12 months than policemen. Men reported greater personality difficulties than women, according to the alternative Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) model for personality disorders. Personality dysfunction was the most robust predictor of poor resilience (ß = −0.465; p < 0.001). Originality/value Personality fragilities appear to have an important negative impact on the resilience of police officers, over and above protective factors and gendered experiences. Interventions targeting emotion regulation, self-appraisal and self-reflection could help promote resilience and foster well-being in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".