Group Versus Individual Diabetes Education for Persons With Experience of Homelessness in Canada
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to explore various forms of diabetes self-management education (DSME), including group and individual sessions, for persons with lived experiences of homelessness (PWLEH) in Canada. METHODS: A qualitative descriptive study using open-ended interviews with health care and homeless sector service providers was utilized to serve those experiencing homelessness in 5 cities across Canada. NVivo qualitative data analysis software was used to facilitate thematic analysis, focusing on variations in DSME for PWLEH. RESULTS: We conducted interviews with 96 unique health and social care providers. Four themes were identified through focused coding of interviews. First, the use of a harm reduction approach during diabetes education tailored to PWLEH considered patients' access to food, medications, and supplies and other comorbidities, including mental health and substance use disorders. The second theme related to the unsuitability of the curriculum in mainstream diabetes education in a group setting for PWLEH. Third, the role of group education in community building is to create supportive relationships among members. The final theme was the importance of trust and confidentiality in DSME, which were most easily maintained during individual education, compared to group formats. CONCLUSIONS: Overall, PWLEH experience unique challenges in managing diabetes. DSME adapted to these individuals' unique needs may be more successful and could be delivered in both individual and group settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".