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Record W4300577672 · doi:10.22374/ijmsch.v5isp1.70

Men’s Health Across the Lifespan: Health and Wellbeing of Older Male Prisoners

2022· article· en· W4300577672 on OpenAlexvenueno aff
Sarah Charlotte Elizabeth Lawrence, Paula Devine

Bibliographic record

VenueInternational Journal of Men s Social and Community Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonLife expectancyGerontologyPublic healthMedicinePsychologyPopulationCriminologyEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Introduction: Older men aged 50 years and over are the fastest-growing cohort in the prisons of the United States (US) and the United Kingdom (UK). This reflects wider demographic change, such as increased life expectancy, as well as harsher sentencing policies, and an increased eagerness of courts to pursue historical offenses particularly relating to sexual crimes. Research has shown that older men in prison often experience poorer physical health than younger prisoners and those with similar age in the general public. However, to date, no such study has explored the health-related needs of older men held in Northern Ireland prisons. The aim of this research was to explore the health and wellbeing needs of older men held in custody in Northern Ireland. Method: A questionnaire was completed by 83 men aged 50 years or over, who were in prison in Northern Ireland in 2016. Comparisons were made with similar community-based surveys. Results: The data showed that on many indicators, older prisoners experience worse health than their peers living in the community. Conclusion: These findings suggest that there is a need for appropriate healthcare planning for older men in prison which recognizes how their health may differ from other age cohorts within prison, as well as from those living outside a custodial establishment.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
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 head0.047
GPT teacher head0.411
Teacher spread0.363 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Men s Social and Community HealthSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207