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A Short-term Longitudinal Analysis of Leisure Coping Used by Police and Emergency Response Service Workers

2002· article· en· W1614008152 on OpenAlexaff
Yoshi Iwasaki, Roger C. Mannell, Bryan Smale, Janice Butcher

Bibliographic record

VenueJournal of Leisure Research · 2002
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of WaterlooUniversity of Manitoba
Fundersnot available
KeywordsCoping (psychology)Term (time)PsychologyService (business)Applied psychologySocial psychologyBusinessMarketingClinical psychology

Abstract

fetched live from OpenAlex

Despite the growth of leisure coping research, an important yet neglected idea is whether or not and how leisure contributes to coping with stress above and beyond the effects of general coping; that is, coping not directly associated with leisure (e.g., problem-focused coping). The purpose of the present study was to examine the contributions of leisure to coping with stress and maintaining good physical and mental health among workers of police and emergency response services when the effects of general coping were taken into account. According to hierarchical regression analyses, leisure coping showed a positive relationship with both short-term and longer-term outcomes of stress and coping above and beyond the contributions of general coping. It is worth emphasizing that mental health was significantly predicted only by leisure coping, not by general coping. The use of leisure for enhancing mood and facilitating palliative coping was found to significantly predict coping effectiveness, satisfaction with coping, and stress reduction. The facilitation of palliative coping and companionship through leisure was related to good mental health, whereas high leisure empowerment was associated with better physical health. Implications of the findings and future research perspectives on leisure coping are discussed.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.159
GPT teacher head0.442
Teacher spread0.283 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations64
Published2002
Admission routes1
Has abstractyes

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