Illustrating stories of stigma from the perspective of people living with diabetes while experiencing homelessness: An arts‐based community participatory research project
Bibliographic record
Abstract
AIMS: Our objective was to explore the stigma experienced by people with lived experiences of both diabetes and homelessness. METHODS: This community-based participatory research (CBPR) project was underpinned by a partnership between academic researchers and those with lived experience (co-researchers). We used two arts-based research methods, Forum Theatre and Participatory Filmmaking, and collected field notes, interviews with co-researchers, and the narrative scripts from the play and film. Interpretative analysis was used to generate broader themes. RESULTS: We identified three themes describing the experience of diabetes stigma while experiencing homelessness, including: (i) limited knowledge of diabetes in the broader community, (ii) the lack of privacy in the shelter environment, and (iii) substance use stereotypes associated with homelessness. Experiences of diabetes and homelessness stigma consisted of instances characterised by judgement, unfair treatment, and skepticism imparted by others. The experience of diabetes stigma was heightened in the context of homelessness, creating situations exacerbated by the judgement and blame passed onto those with diabetes in the shelter. CONCLUSION: Diabetes stigma has a profound mental and emotional impact on individuals experiencing homelessness, often impacting their ability to manage their condition. Future research is needed to explore these intersections in different contexts and develop comprehensive strategies that may mitigate stigma and improve the well-being of individuals with diabetes experiencing homelessness.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".