Exploring the Lived Experience of Diabetes Through an Intersectional Lens: A Qualitative Study of Adults With Type 1 and Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Our aim in this study was to explore the lived experience of adults living with type 1 and type 2 diabetes through an intersectional sex- and gender-based analysis plus lens. METHODS: Qualitative interviews with 15 adults (9 women, 6 men) were conducted in February and March 2021. Interviews were recorded, transcribed and analyzed for semantic and latent themes noting differences in participants' accounts of living with diabetes by gender, age, race and ethnicity, type of diabetes and other key demographics. RESULTS: Participants' experiences differed substantively by gender, age and racialization. "Resilience" was identified as a central feature in participants' lives. Factors that contributed to resilience included supportive relationships, a feeling of agency and social acceptance; confounding factors included unsupportive relationships, a lack of agency and experiences of stigma, discrimination and microaggressions. CONCLUSIONS: Lived experiences of diabetes can best be understood through an intersectional lens that considers peoples' diverse socioeconomic locations and identities. Those who experience discrimination, including women, older individuals and racialized people, may also experience the compounding effects of multiple marginalization, requiring greater investment in factors that contribute to their resilience. Considering the varied needs of diverse individuals should be integrated into routine diabetes care.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".