Perceived organizational support and its interaction with voice on police officers' organizational cynicism, stress and emotional exhaustion
Bibliographic record
Abstract
Purpose Among a sample of 281 active-duty Canadian police officers, the current study investigated whether perceived organizational support (POS) would predict officers' organizational cynicism, stress and emotional exhaustion three months later. The moderating influence of officer voice on these relationships was also examined. Design/methodology/approach In collaboration with a large policing organization, online surveys collecting quantitative data and soliciting open-ended comments were administered to officers, with a three-month lag separating survey administrations. Findings The results reveal that POS predicted significant variance in each of the investigated outcomes. It was found that voice moderated the association between POS and organizational cynicism, but in a manner that suggests a suboptimal voice climate within the organization. Officers provided open-ended qualitative comments that supported this interpretation. Practical implications The evidence supports that if organizational leaders wish to prevent disadvantageous outcomes such as organizational cynicism, stress, emotional exhaustion and their consequents, then advancing both organizational support and a positive voice climate is recommended. Originality/value The results suggest that voice interacts with POS to influence organizational cynicism among police, highlighting the importance of responsiveness to voice for police management, and thus serving as an important bridge between theory and practice.
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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 | 0.001 | 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) | 0.002 | 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".