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Record W4407917929 · doi:10.1080/01490400.2025.2465741

Labor, Depletion, and Disconnection: Leisure as Restoration for Uniformed Public Safety Personnel

2025· article· en· W4407917929 on OpenAlexaffabout
Jaylyn Leighton, Kimberly J. Lopez

Bibliographic record

VenueLeisure Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDisconnectionBusinessLabour economicsMarketingEconomicsPolitical science

Abstract

fetched live from OpenAlex

In Canada, uniformed public safety personnel (PSPs) are systemically uniformed and placed into positions where they are exposed to both primary and/or secondary/vicarious trauma while “on duty.” PSPs, particularly law enforcement officers (police) also navigate historical and cultural traumas and oppressions that are symbolized by the materiality of the uniform. Menakem’s work delves into the profound impact of intergenerational trauma that lives in white, Black, and (uniformed) police bodies, and how healing must begin on an individual level before it can extend to a collective healing. Drawing on research from a study conducted with PSPs, this paper helps to address the tensions surrounding labour, trauma, and wellness faced by individuals systematically uniformed to perform roles in public safety and care between government and community. Specifically, we highlight three narratives themes, (1) “It is not if, it is when”—The Inevitable Nature of Trauma Labour, (2) “You should be able to handle anything”—Internalizing Oppressive Mental and Emotional Narratives, and (3) “Deal with your shit on your own time”—Leisure as a Practice of Care, a Space of Healing, and Site for Restoration. We discuss leisure as restoration to promote healing for uniformed PSPs who endure trauma as part of their labours (individual healing), as a means of creating a rippling effect that fosters collective healing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.861

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.334
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2025
Admission routes2
Has abstractyes

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