Quality over Quantity: Assessing the Impact of Frequent Public Interaction Compared to Problem-Solving Activities on Police Officer Job Satisfaction
Bibliographic record
Abstract
Abstract Research outside the field of policing has shown that job satisfaction predicts job performance. While policing research has demonstrated performing community-oriented policing (COP) activities generally improves police officer job satisfaction, the mechanism through which it occurs remains unclear. This study contributes to the community-policing literature through a survey of 178 police officers at the Toronto Police Service. The survey instrument measures the mechanism through which job satisfaction is impacted. Results indicate that primary response officers are more likely to be somewhat or very unsatisfied with their current job assignment compared with officers with a COP assignment—confirming what previous research has found. Further, those who interact with the public primarily for the purpose of engaging in problem-solving are more likely to be very satisfied with their current job assignment compared with those who do so primarily for the purpose of responding to calls for service. Engaging in problem-solving increases the odds of being very satisfied in one’s job assignment, and the combination of frequent contacts with the public and problem-solving is less important than problem-solving alone. The implications of the study findings for COP strategies are 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 | 0.004 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".