Using concept mapping to prioritize barriers to diabetes care and self-management for those who experience homelessness
Bibliographic record
Abstract
BACKGROUND: Diabetes is a chronic medical condition which demands that patients engage in self-management to achieve optimal glycemic control and avoid severe complications. Individuals who have diabetes and are experiencing homelessness are more likely to have chronic hyperglycemia and adverse outcomes. Our objective was to collaborate with individuals experiencing homelessness and care providers to understand the barriers they face in managing diabetes, as a first step in identifying solutions for enhancing diabetes management in this population. METHODS: We recruited individuals with lived experience of homelessness and diabetes (i.e. clients; n = 32) from Toronto and health and social care providers working in the areas of diabetes and/or homelessness (i.e. providers; n = 96) from across Canada. We used concept mapping, a participatory research method, to engage participants in brainstorming barriers to diabetes management, which were subsequently categorized into clusters, using the Concept Systems Global MAX software, and rated based on their perceived impact on diabetes management. The ratings were standardized for each participant group, and the average cluster ratings for the clients and providers were compared using t-tests. RESULTS: The brainstorming identified 43 unique barriers to diabetes management. The clients' map featured 9 clusters of barriers: Challenges to getting healthy food, Inadequate income, Navigating services, Not having a place of your own, Relationships with professionals, Diabetes education, Emotional wellbeing, Competing priorities, and Weather-related issues. The providers' map had 7 clusters: Access to healthy food, Dietary choices in the context of homelessness, Limited finances, Lack of stable, private housing, Navigating the health and social sectors, Emotional distress and competing priorities, and Mental health and addictions. The highest-rated clusters were Challenges to getting healthy food (clients) and Mental health and addictions (providers). Challenges to getting healthy food was rated significantly higher by clients (p = 0.01) and Competing priorities was rated significantly higher by providers (p = 0.03). CONCLUSIONS: Experiencing homelessness poses numerous barriers to managing diabetes, the greatest of which according to clients, is challenges to getting healthy food. This study showed that the way clients and providers perceive these barriers differs considerably, which highlights the importance of including clients' insights when assessing needs and designing effective solutions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".