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Record W3018771166 · doi:10.35502/jcswb.122

Prison Health as Public Health in Ontario Corrections

2020· article· en· W3018771166 on OpenAlexafffundvenueabout
Yoko Murphy, Howard Sapers

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSimon Fraser UniversityUniversity of Ottawa
FundersGovernment of Ontario
KeywordsGovernment (linguistics)PrisonPublic healthHealth careMental healthObligationPublic health lawPolitical scienceHealth policyPublic administrationMedicineEnvironmental healthNursingInternational healthLawPsychiatry

Abstract

fetched live from OpenAlex

The majority of incarcerated individuals in Canada, and especially in Ontario provincial correctional institutions, are released into the community after a short duration in custody. Adult correctional populations have generally poor health, including a heightened prevalence of mental health and substance use disorders. There are legal and ethical obligations to address health care needs of incarcerated individuals, and also public health benefits from ensuring adequate, appropriate, and accessible health services to individuals in custody. The Independent Review of Ontario Corrections recommended the transformation of health care in Ontario provincial corrections in 2017, including transferring health service responsibilities to the Ministry of Health and Long-Term Care. The Correctional Services and Reintegration Act, 2018, would affirm the provincial government’s obligation to provide patient-centred, equitable health care services for individuals in custody. We encourage the Government of Ontario to proclaim the Act and continue the momentum of recent reform efforts in Ontario.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.134
Threshold uncertainty score0.975

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0090.005
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.055
GPT teacher head0.333
Teacher spread0.278 · 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 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

Citations8
Published2020
Admission routes4
Has abstractyes

Explore more

Same venueJournal of Community Safety and Well-BeingSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207