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Record W4403861838 · doi:10.1080/14927713.2024.2420795

Leisure education and parks programming to address stress, burnout, and coping in law enforcement officers: an exploratory study

2024· article· en· W4403861838 on OpenAlexvenueno aff
Clara Hawkes, Rhonda Nelson, Melissa Zahl, Dorothy L. Schmalz

Bibliographic record

VenueLeisure/Loisir · 2024
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutLaw enforcementExploratory researchCoping (psychology)PsychologyEnforcementJob stressApplied psychologyCriminologyPolitical scienceLawSociologySocial psychologyClinical psychologyJob satisfactionSocial science

Abstract

fetched live from OpenAlex

Law enforcement officers (LEOs) frequently experience occupational stress, which may lead to burnout. Without positive coping strategies, LEOs are at risk of a myriad of negative health-related issues. This exploratory study evaluated the impact of a leisure education and parks programme offered in the workplace on stress, burnout, and coping in LEOs. Ten LEOs enrolled in the six-week programme that offered group leisure education sessions supplemented by individual time spent in parks. Scores on the Operational Police Stress Questionnaire (PSQ-Op) and Maslach Burnout Inventory-Human Services Survey (MBI-HSS) revealed lower stress and burnout scores for participants following the program, but the changes were not statistically significant. One subscale of the COPE Inventory, suppression of competing activities, revealed significantly higher scores post-programme (p =.04). The majority (77.78%) of the participants reported that they valued and were satisfied with the program. Findings provide initial support for leisure education and parks programming provided to LEOs in an effort to address their occupational health needs including stress, burnout, and coping.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.342
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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