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Record W2004046129 · doi:10.1177/1757975914547922

Participatory health research within a prison setting: a qualitative analysis of ‘Paragraphs of passion’

2014· article· en· W2004046129 on OpenAlexaffabout
Vivian R. Ramsden, Ruth Elwood Martin, Jennifer McMillan, Alison Granger‐Brown, Brenda Tole

Bibliographic record

VenueGlobal Health Promotion · 2014
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of British ColumbiaUniversity of Saskatchewan
Fundersnot available
KeywordsPrisonPassionCitizen journalismQualitative researchPsychologyParticipatory action researchQualitative analysisPolitical scienceMedicineSociologyCriminologySocial psychologySocial scienceLaw

Abstract

fetched live from OpenAlex

The purpose of this research was to engage, empower and enhance the health and well-being of incarcerated women. The integration of primary health care, community-based participatory research, a settings approach to health promotion, and transformative action research guided the design of this study. A partnership between incarcerated women who became peer-researchers, correctional staff, and academic researchers facilitated the equitable contribution of expertise and decision-making by all partners. The study was conducted in a short sentence (two years or less), minimum/medium security Canadian women's correctional centre. Of the approximately 200 women that joined the research team, 115 participated in writing a 'paragraph of passion' while incarcerated between November, 2005 and August, 2007. Participatory, inductive qualitative, narrative and content analysis were used to illuminate four themes: expertise, transformation, building self-esteem, as well as access and support. The women organized monthly health forums in the prison to share their new knowledge and life experience with other incarcerated women, correctional staff, academics, and community members, and in doing so have built bridges and relationships, some of which have lasted to the present day.

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)0.044
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0020.000
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 head0.447
GPT teacher head0.649
Teacher spread0.202 · 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 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

Citations13
Published2014
Admission routes2
Has abstractyes

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