It’s not all about guns and gangs: role overload as a source of stress for male and female police officers
Bibliographic record
Abstract
This research uses male (n = 1169) and female (n = 300) samples of police officers and multivariate techniques to test a model which hypothesises: (1) work-role overload and family-role overload will predict stress, (2) objective (i.e. hours employed) and subjective (i.e. non-supportive culture, pressures to perform work outside their mandate, competing demands) work demands will predict work-role overload, (3) objective (i.e. dependent care hours) family demands will predict family-role overload and (4) gender differences across all paths. Results showed the relationship between work-role overload and stress was stronger for male police officers, whereas the relationship between family-role overload and stress was stronger for female police officers. Hours employed, performing work outside one’s mandate, and perceptions of the work culture as non-supportive were stronger predictors of work-role overload for the female officers in our sample than for their male counterparts. The path between hours in dependent care hours and family-role overload was also stronger for female than male police officers. Competing work demands, on the other hand, was a stronger predictor of work- role overload for male than female police officers.
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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 | 0.004 |
| 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 | 0.001 |
| 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".