Exploring the role of compassion satisfaction and compassion fatigue in predicting burnout among police officers
Bibliographic record
Abstract
Prior research on police practices has highlighted the plethora of operational and organisational stressors that police officers face throughout their careers. Work-related demands, acute stress, and lack of organisational support and resources may lead officers to experience burnout, which is characterised by emotional exhaustion, lack of professional efficacy, depersonalisation, and cynicism. Prior research studies have consistently demonstrated burnout's significant impact on police officers’ mental and physical health, and they have also shown that traumatisation (specifically compassion fatigue) appears to be associated with burnout among police officers. The present study aims to examine the prevalence of burnout among police officers and to identify the association of burnout with compassion fatigue, compassion satisfaction, and years of experience. Data collection occurred in cooperation with the National Police of Finland, and all officers who may potentially experience work-related trauma were invited to participate. Study participants were police officers from the National Police of Finland (n=1, 173). Compassion Satisfaction and Fatigue Test and demographics questions. Data analyses indicated that most study participants (78.03% or n=945) reported low levels of burnout. Moreover, burnout was found to be significantly positively correlated with compassion fatigue (r=0.76; p<0.01) and years of experience (r=0.10; p<0.01), but significantly negatively correlated with compassion satisfaction (r=-0.49; p<0.01). Furthermore, hierarchical linear regression indicated that years of experience, compassion satisfaction, and compassion fatigue were significant predictors of burnout. Authors discuss various interpretations, implications, and limitations of the current study's findings, as well as providing recommendations for future research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".