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Record W4402901844 · doi:10.1097/jfn.0000000000000514

Pain During Incarceration: Searching for Safety in Hypermasculine Correctional Settings

2024· article· en· W4402901844 on OpenAlexaff
Duncan Stewart MacLennan, Gerri Lasiuk, Diane Kunyk, Maria Mayan

Bibliographic record

VenueJournal of Forensic Nursing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsPrisonPsychologySocial isolationContext (archaeology)Social psychologyCriminologyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Hypermasculine prison culture produces hierarchies based on individuals' ability to assert dominance through strength and violence. Pain can impact physical strength, thereby limiting the ability to elevate or maintain social status within such hierarchies. AIM: The aim of the study was to explore the pain experiences of incarcerated men who were embedded into hypermasculine prison culture. METHODS: Interpretive description co-positioned with relational ethics informed the study design. Twelve males participated in 1-hour interviews, which were transcribed verbatim and analyzed inductively to identify themes. The data analysis occurred in three phases: sorting and organizing, making sense of patterns, and transforming patterns into findings. RESULTS: We found that correctional settings were characterized by a social culture of toxic hypermasculinity that influenced the pain experiences of people who were incarcerated. Individuals who experienced pain were more vulnerable to self-isolation in situations in which pain threatened their ability to defend themselves and their previously established social status within the prison hierarchy. Yet, those who felt secure in subordinate hierarchical positions and had effective peer relationships perceived greater safety in communal settings, which proved to be a distraction from pain. CONCLUSION: In the context of incarceration, positive peer relationships and perceptions of safety positively influenced individuals' experience of pain.

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 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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.333
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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