The challenges of managing diabetes while homeless: a qualitative study using photovoice methodology
Bibliographic record
Abstract
<h3>BACKGROUND:</h3> Minimal consideration has been given to understanding the challenges of managing diabetes while homeless from the perspective of those with lived or living experience. We used a community-based participatory approach to explore these challenges. <h3>METHODS:</h3> We recruited coresearchers with experiential knowledge of both homelessness and diabetes. Lead researchers conducted research training and facilitated research development by coresearchers. Coresearchers collectively chose to use photovoice methodology to illustrate the challenges of accessing healthy food while homeless and to explore how homelessness more broadly affects diabetes management. After training in photography technique and ethics, coresearchers took photos to address these objectives and created accompanying narratives using photo elicitation techniques. Lead researchers analyzed photos and narratives, and extracted themes, refined through group discussion. <h3>RESULTS:</h3> The 8 coresearchers had type 2 diabetes (diagnosed 18 months to 23 years previously) and had experienced homelessness for periods ranging from 8 months to 12 years. We identified 4 themes from the 17 photos and narratives they produced. Homelessness imposed major demands on emotional and mental health, impairing the ability of those affected to focus on diabetes self-management. Foods provided in shelters were often nutritionally poor or unpalatable. Obtaining housing facilitated diabetes management through stability and autonomy, but cost and lack of knowledge posed challenges to healthy food preparation. Homelessness also presented challenges to accessing diabetes care professionals and prescription medications. <h3>INTERPRETATION:</h3> The images and narratives provide a powerful firsthand, in-depth account of the challenges faced by people trying to manage diabetes while homeless. Understanding these challenges is the first step in enabling providers and policy-makers to meet the needs of this population.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".