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Record W2531451088 · doi:10.1177/1078345816669963

Health Promotion Body Maps of Criminalized Woman

2016· article· en· W2531451088 on OpenAlexaff
Lorie Donelle, Jodi Hall

Bibliographic record

VenueJournal of Correctional Health Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsFanshawe CollegeWestern University
Fundersnot available
KeywordsMedicinePromotion (chess)Health promotionNursingCriminologyEnvironmental healthPublic healthLawPolitics

Abstract

fetched live from OpenAlex

Health promotion is the process of enabling people to increase control over, and to improve, their health. For criminalized women, opportunities to engage in health-promoting activities are obstructed by factors related to the context of their lives prior to and during incarceration. The purpose of this study was to gain insight into criminalized women's health and their access to health information and services. Thematic data analysis of body maps and interview transcripts revealed a central theme related to barriers and facilitators to health resources as contingent on being "inside" or "outside" of the incarceration setting. Incarceration holds the possibility for women to access health care not readily available in the community, or because women were not in the position to receive supports. The absence of timely health-promoting practices while incarcerated could be characterized as missed opportunities to support this marginalized, underserved population.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.048
GPT teacher head0.448
Teacher spread0.400 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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