Effort-Reward Imbalance, Overcommitment, and Psychological Distress in Canadian Police Officers
Bibliographic record
Abstract
The purpose of the present study was to examine the relationship among Effort, Reward, and Overcommitment dimensions of Siegrist's Effort-Reward Imbalance Model and Psychological Distress in a sample of 78 Canadian police officers. Ages of respondents ranged between 24 and 56 years (M=36.1, SD=8.0). 30% of respondents had been in policing for 16 years or more, 24% between 6 and 15 years, and 44% for 5 years or less. Ordinary least-squares regression was used to evaluate the relationship between the independent and dependent variables. After adjusting for age, sex, education, and marital status, higher levels of Effort-Reward Imbalance and Overcommitment were associated with greater Psychological Distress. Present findings support the utility of the model in this particular occupational group and add to the increasing literature suggesting association of Effort-Reward Imbalance, Overcommitment, and reduced mental health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".