Examining the Relationship Between Personality Traits, Compassion Satisfaction, and Compassion Fatigue Among Police Officers
Bibliographic record
Abstract
Police officers are often exposed to violence and potentially traumatic encounters, but they have not been a focus of research on compassion fatigue or compassion satisfaction. The current study examines compassion fatigue and satisfaction among police officers and how these variables are influenced by negative personality traits. This study’s participants were police officers ( n = 1,173) from the National Police of Finland, and its aims were twofold: (a) to explore the prevalence rates and relationships between compassion fatigue, compassion satisfaction, burnout, and personality traits (Machiavellianism, `narcissism, psychopathy) among study participants; and (b) to explore whether compassion satisfaction, burnout, years of police experience, and negative personality traits are predictors of compassion fatigue. The results of the current study indicated that 10% of police officers indicated high levels of compassion fatigue and 40% revealed low levels of compassion satisfaction. In addition, compassion fatigue was found to be negatively correlated with compassion satisfaction ( r = −.33, p < .01), whereas negative personality traits were positively correlated with compassion fatigue (Machiavellianism: r = .20; narcissism: r = .19; psychopathy: r = .23; p < .01). Furthermore, negative personality traits (except narcissism) were negatively correlated with compassion satisfaction (Machiavellianism: r = −.22; psychopathy: r = −.32). Structural equation modeling (SEM) was performed to assess predictors of compassion fatigue and it indicated good model fit to the data (goodness of fit index, GFI = .976; comparative fit index, CFI = .934; root mean square error of approximation, RMSEA = .092; standardized root mean square residual, SRMR = .421). In addition, SEM revealed that compassion satisfaction, burnout, and personality traits (Machiavellianism, narcissism, and psychopathy) were significant predictors of compassion fatigue. Clinical and training implications as well as future research recommendations are also discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".