Participatory health research within a prison setting: a qualitative analysis of ‘Paragraphs of passion’
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".