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Record W4406675170 · doi:10.1186/s40352-024-00308-2

Correctional officers and the ongoing health implications of prison work

2024· review· en· W4406675170 on OpenAlexaff
William J. Schultz, Rosemary Ricciardeli

Bibliographic record

VenueHealth & Justice · 2024
Typereview
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of NewfoundlandMacEwan University
Fundersnot available
KeywordsSocial policyPrisonPublic healthWork (physics)CriminologyPublic health lawHealth policyPolitical sciencePublic administrationSociologyPsychologyInternational healthMedicineNursingLaw

Abstract

fetched live from OpenAlex

Correctional Service Providers (CSP), including Correctional officers (COs), are key front-line figures in prisons globally, with responsibility for a wide range of daily prison operations. Over the past decade, research on prison staff has massively grown. However, the portrait this scholarship draws is concerning. Research focusing on the physical, mental, and social wellbeing of prison staff consistently paints a picture of a deeply unhealthy group of people, with above-average levels of physical health concerns. Likewise, recent literature suggests correctional employees are facing a mental health crisis, with high prevalence of mental health disorders and self-harming behaviors, even when compared to other law enforcement personnel. Further, scholars have expressed concerns about the social and cultural wellbeing of staff, factors that directly impact daily prison operations. We conduct a broad overview of the literature on correctional worker health and wellness, identifying key themes and major areas of concern. We conclude by identifying key challenges and proposing areas for future research.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.101
GPT teacher head0.462
Teacher spread0.362 · 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 designOther design
Domainnot available
GenreReview

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

Citations17
Published2024
Admission routes1
Has abstractyes

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