Willingness to use follow-up eye care services after vision screening in rural areas surrounding Chennai, India
Bibliographic record
Abstract
AIMS: To assess the willingness to utilise follow-up eye care services among participants of community vision screenings in rural villages surrounding Chennai. METHODS: Vision screening participants aged ≥40 years were selected by systematic sampling and were invited to respond to a pretested verbal survey with close-ended questions before undergoing screening. RESULTS: Two hundred and ninety-two people responded. Among the respondents, 50.3% reported experiencing an eye problem, and 53% of these individuals had never had an eye examination. Acceptance rate for eye surgery, medications, and eyeglasses among the respondents was 59.2%, 52.7% and 90.8%, respectively. These acceptances were not associated with sex, age, or employment; medication acceptance was inversely associated with literacy. Surgery acceptance and medication acceptance were associated with area of residence. Presence of another chronic disease was a predictor for surgery acceptance among respondents experiencing eye problems. CONCLUSIONS: Maintaining consistent quality of services delivered is crucial for increasing uptake of existing eye care services. Educational interventions may increase eye care service usage by targeting all demographic subgroups of rural populations equally. Additional interventions should be offered to patients without previous exposure to the healthcare system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".