Understanding why patients with cataract refuse free surgery: the influence of rumours in Kenya
Bibliographic record
Abstract
OBJECTIVES: To understand the reasons that hinder people from uptake of sponsored cataract surgery. METHODS: A mixed methods (qualitative and quantitative) approach was used. During routine screening activities at Kwale District, Kenya, local residents with visually impairing cataract were clinically assessed and offered free surgery. Interviews were conducted using a semi-structured guide that covered different aspects related to acceptance of cataract surgery including knowledge of others who underwent surgery and their outcome. Analysis focused on differences between people accepting and people refusing surgery and the reasons for non-acceptance of surgery. RESULTS: Ninety interviews were conducted, 48 with people accepting and 42 with people refusing free surgery. Those who accepted surgery generally reported good outcome in others, while people who refused surgery often reported to know someone who worsened or even become blind after surgery. Many of these 'failed cases' were prominent figures in the local community, and most of them had already died. Glaucoma was the single most common underlying medical condition. On being re-interviewed, several people admitted that they had actually never met someone who had unsuccessful surgery but only heard rumours. CONCLUSION: In Africa, a rumour of blinding eye surgery is not uncommonly being used by patients to justify their refusal to have cataract surgery. Underlying reasons appear to be related to shame, fear of surgery or missing social support. Improved awareness of the general population regarding eye conditions and their management, involvement of the family and local community in decision making, good surgical outcomes and appropriate counselling are possible methods to enhance acceptance.
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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 | 0.004 | 0.017 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".