Diabetes in the context of incarceration: a scoping review
Bibliographic record
Abstract
Background: The burden of chronic conditions, like diabetes, is disproportionately carried by people facing social disadvantages (e.g., those with experiences of incarceration). A dearth of knowledge remains about this topic. We conducted a scoping review to determine the extent of literature about diabetes management and/or self-management in relation to incarceration. Methods: We used the Arksey and O'Malley five stage process, recommendations by Levac et al., and the PRISMA Extension for Scoping Reviews Checklist. Core search terms for diabetes were combined using the Boolean operator AND with terms relevant to incarceration. We initially searched the following electronic academic databases on January 5, 2021, and then updated these searches on September 7, 2022: APA PsycInfo, CINAHL, Criminal Justice Abstracts, EMBASE, MEDLINE, Scopus, and SocINDEX. There were no restrictions on language, study design, quality, location, time, and sex or gender differences. We searched for research articles, conference proceedings, dissertations and theses, government documents, and organization documents. We then searched for other forms of literature using an electronic database (ProQuest Dissertations and Theses - Global), the internet search engine Google, and various corrections and diabetes websites in August 2021 and then updated these searches in September 2022. We also reviewed the reference lists of the final selected documents to identify additional literature. Findings: The search from the seven databases identified 3076 records. The search from other sources (e.g., websites) identified an additional 1077 records. A total of 40 documents met our final inclusion criteria and were included in this review. The type of research conducted was primarily quantitative in nature. Clinic and education interventions were most commonly investigated. Clinical outcomes were often reported. Most guidelines were targeted at healthcare providers. Much of the literature originated from high-income countries, which may not be fully applicable for different contexts like low-income countries. Many interventions were associated with improved outcomes. Interpretation: Administrators can use our findings to develop appropriate policies for this population. Tailored diabetes education for this population and healthcare providers may improve management practices. Our findings offer key insights for improving diabetes care and outcomes for this underserved population. Addressing the diabetes-specific health needs of these people may improve overall public health. Funding: KD has received the O'Brien Institute for Public Health Postdoctoral Scholarship (University of Calgary), Cumming School of Medicine Postdoctoral Scholarship (University of Calgary), and the Libin Cardiovascular Institute's 2021 Person to Population Seed Grant (University of Calgary).
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How this classification was reachedexpand
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".