Cardiovascular Risk Factors Among Prisoners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND AIM: Incarceration is characterized by inequalities in disease burden and an increased risk for cardiovascular disease (CVD). The aim of this review was to critique published empirical research studies on cardiovascular risk factors among prisoners and to summarize and synthesize current knowledge and findings across these studies. DESIGN AND REVIEW METHOD: An integrative review of the studies was conducted. Cooper's five stage method was used as a framework to guide data collection, analysis, and synthesis. Quality appraisal of retrieved studies was done using a combined evaluation tool for quantitative research studies and a checklist. The following databases were searched: CINAHL, MEDLINE, PubMed, Cochrane, Indigenous Studies Portal (iPortal), Native Health Database, Criminal Justice Abstracts, and PsychInfo using keywords. Inclusion criteria were used to select published papers. RESULTS AND CONCLUSION: A total of 12 studies that met the inclusion criteria were identified and analyzed. Hypertension, among other CVD risk factors such as smoking, physical inactivity and obesity, was one of the three most common CVD risk factors found in prisoners. Women and young offenders had a higher prevalence of hypercholesterolemia. Identifying prevalent risks factors among prisoners might influence the development of CVD prevention strategies that are specifically directed to at risk prisoners.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it