Enablers and barriers to diabetic retinopathy eye care among first nations and Métis women
Bibliographic record
Abstract
BACKGROUND: Diabetes is increasingly prevalent in Indigenous women and increases their risk of developing diabetic retinopathy, an eye complication of diabetes and a common cause of vision loss in Canada, especially among adults. Early detection is the most effective approach to prevent vision loss and reduce the impact of diabetic retinopathy. OBJECTIVE: This study examined enablers and barriers that influence the diabetes eye care behaviour of First Nations and Métis women with diabetes and at risk of diabetes. METHODS: We conducted a descriptive qualitative study with 35 First Nations and Métis women with diabetes or at risk of diabetes in Saskatoon, Canada. Data were collected via four sharing circle discussions and were analysed using thematic analysis. RESULTS: The study findings showed that understanding of diabetes eye care access and cost, and unsupportive interactions with health care practitioners, were barriers to diabetic retinopathy care behaviour. Conversely, the presence of eye complications, participants' resolve to manage diabetes, self-efficacy and fear due to experiences of family members with diabetes enabled diabetes eye care. CONCLUSIONS: Our study advances knowledge in socio-cultural factors influencing diabetic retinopathy care behaviour among First Nations and Métis women living with and at risk of diabetes. The study shows the need for further public health and health system interventions to address barriers and support Indigenous peoples with or at risk of diabetes to make informed health decisions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".