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Record W2575249361 · doi:10.1177/1078345816685707

Chronic Health Conditions Among Incoming Canadian Federally Sentenced Women

2017· article· en· W2575249361 on OpenAlexafffundabout
Amanda Nolan, Lynn A. Stewart

Bibliographic record

VenueJournal of Correctional Health Care · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMinistry of Community Safety and Correctional Services
FundersHealth Canada
KeywordsMedicineOccupational safety and healthChronic hepatitisPsychiatryFamily medicineGerontologyVirus

Abstract

fetched live from OpenAlex

Over a 13-month period, health data on all consecutive incoming Canadian federally sentenced women offenders were collected and analyzed ( N = 280). The most common health conditions cited were back pain, head injury, hepatitis C virus (HCV), and asthma. Rates of chronic health problems were generally similar to those of their male offender counterparts, with the notable exception of HCV, which was higher for women. Aboriginal women offenders had particularly high rates of HCV. The study provides valuable information on the self-reported physical health status of federally sentenced women offenders that can be used as a benchmark to examine health trends over time.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.370
Teacher spread0.351 · 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

Citations21
Published2017
Admission routes3
Has abstractyes

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