Therapy dogs, stress-reduction, and well-being within the detachment: Interviews with law-enforcement personnel
Bibliographic record
Abstract
Abstract Despite the increasing popularity of canine-assisted interventions (CAIs) across a variety of contexts, and the demonstrated efficacy they have on stress reduction, there is a paucity of research exploring the implementation of such programs within the context of law enforcement. Policing is known to be an environment in which personnel experience elevated occupational stress. As stress can compromise the overall well-being of law-enforcement personnel and their ability to serve the public, it is important to understand how CAIs involving therapy dog visitation are experienced by the very individuals for whom they are intended to help. This qualitative study explored Royal Canadian Mounted Police (RCMP) members’ experiences of stress within a detachment and their perceptions of a CAI integrated within their workplace to support their well-being. Eight RCMP members (75% female, Mage = 49.21, SD = 6.12) from an urban RCMP detachment situated in the downtown core of a small city were interviewed. The interview focused on topics including members’ experiences of stress within the workplace, their perceptions of the effects of stress, resources and sources of support, and their experiences participating in a weekly CAI within their detachment. Results indicated that organizational stressors were identified more often than operational stressors; that members most commonly reported that stress rendered them more irritable and impacted their sleep, and that the most commonly reported coping mechanism and resource are exercise and medical services, respectively. In discussing the CAI, participants were overwhelmingly positive in their experiences of the program, and demonstrated support for having CAIs within the detachment setting. Discussion of the impact of the sessions demonstrated that participants found that the program served as a break from work, relieved their stress, improved their mood, and helped them to change perspectives. Participants also identified the role of the dogs within the program, which included their role as a social being. The findings from the current study contribute to better understanding of the lived experiences of stress within RCMP members and of CAIs within an applied setting.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".