Predictors of stigma perception by people with type 1 diabetes: A cross-sectional analysis of the BETTER registry
Bibliographic record
Abstract
AIMS: This study investigates stigma predictors across ages and genders, addressing a critical gap in understanding diverse populations to reduce related suboptimal clinical and psychosocial outcomes. METHODS: Cross-sectional analysis of self-reported data from BETTER, a Canadian registry of people with type 1 diabetes. Participants (n = 709) completed the 19-item-Diabetes-Stigma Assessment-Scale (DSAS-1) categorized into treated differently, blame and judgment, and identity concerns sub-scales. Associations with diabetes distress (DDS-17-score/102), depression (PHQ-9-score/27), social-support (ESSI-score/34), fear of hypoglycemia (HFS-II-score/132), and hyperglycemia-avoidance-behaviours (HAS-score/88) were computed. RESULTS: Perceived stigma was highest in youth aged 14-24 years (46·0 ± 15·6, p < 0·001) and women (41·2 ± 15·7, p = 0·009), compared to other age groups and men. Blame and Judgment contributed to most of stigma perception. Youth perceived significantly more blame and judgment (p < 0·001) and identity concerns (p = 0·001) compared to middle-aged adults and seniors. Women perceive significantly more blame and judgment compared to men (p < 0·001). The perception of being treated differently was not reported to be an issue across ages and genders. Participants with higher scores of depression, diabetes-distress, fear of hypoglycemia, hyperglycemia-avoidance behaviours, and lesser social-support, reported increased stigma. CONCLUSIONS: Stigma varies by age and gender, underscoring the need for targeted interventions to reduce it. Challenging stereotypes and reducing stigma-related stressors are essential for better outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".