Factors influencing access to diabetic eye care among people living with diabetes attending a tertiary hospital in Dar es salaam, Tanzania: a qualitative study
Bibliographic record
Abstract
Abstract Background The increase in prevalence of Diabetes has led to a rise in the burden of blindness due to diabetic retinopathy. Timely access to diabetic eye care may prevent blindness due to diabetic complications, reducing the burden of blindness in countries with limited resources like Tanzania. This study aimed to explore the factors influencing access to diabetic eye care among people living with diabetes attending a tertiary hospital. Methods A total of 14 interviews were conducted with people living with diabetes attending a tertiary hospital from March 2023 to May 2023. Participants were purposively selected during their visits to the eye clinic. The interviews were audio-recorded, transcribed verbatim, and analysed by qualitative content analysis. Results Factors influencing access to diabetic eye care among people living with diabetes were reported, including the cost of eye care services, not knowing that diabetes can affect eyes, fear, and having other priorities emerged as barriers to accessing care while referral from primary healthcare workers, eye symptoms, knowing that diabetes can affect eyes, living close to the facility offering eye care services, influence from close relatives, and having health insurance were reported as facilitators to attending diabetic eye care services. Conclusion Educating individuals with diabetes about the importance of diabetic eye care, while addressing the barriers to accessing it, will help them receive timely diabetic eye care and prevent blindness caused by diabetic retinopathy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".