How Children's Advocacy Centers law enforcement officers cope with work-related stress: impacts and approaches to self-care
Bibliographic record
Abstract
Work-related stress has been identified as being harmful for law enforcement officers’ (LEOs) health. The absence of effective coping strategies exacerbates the negative psychophysiological impacts on health. The literature suggests that law enforcement employers and communities also feel the impact of stress among LEOs. This study addresses the gap in the current literature in terms of qualitative-based exploration of the personal and professional impacts of LEs working within Children's Advocacy Centers (CACs) and self-care and stress alleviation practices in response to environmental stressors. CAC LEOs’ responses to three open-ended responses were analyzed from a national survey in the United States. Thematic analysis was utilised to identify emerging themes in relation to the: (1) personal, (2) professional impacts of work-related stress, and (3) the self-care or stress alleviation strategies adopted by LEOs. LEOs face multiple personal and professional stressors that impact their coping behaviours and health outcomes. Variation exists among LEOs in terms of coping behaviours and requires further investigation. This study highlights several gaps in the literature, including the personal and professional impacts of work-related stress among LEOs and the subsequent coping strategies adopted by LEOs in response to stressful working environments. Future research should further explore the impacts of work-related stress, coping strategies, and the development of effective stress prevention reduction approaches for this population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".