Interventions to improve utilization of cataract surgical services by girls: Case studies from Asia and Africa
Bibliographic record
Abstract
PURPOSE: Gender and blindness initiatives continue to make eye care personnel aware of the service utilization inequity strongly favouring men, yet interventions to reduce that inequity, particularly for girls, are under developed. METHODS: This descriptive study gathered quantitative data on the degree of gender equity at five Child Eye Health Tertiary Facilities (CEHTFs) in Asia and Africa and conducted in-depth interviews with eye care personnel to assess their strategies and capacity to reduce gender inequity. Cataract surgery was utilized to assess the degree of inequity and success of interventions to reduce inequity in case finding, service utilization, and follow-up. RESULTS: CEHTF administrative data showed significant gender inequity in cataract surgical services favouring boys in all settings. CEHTFs actively seek children through community and school-based outreach, yet do not have initiatives to reduce gender inequity. Little gender inequity was found among children receiving surgical and follow-up care, although two out of three children were boys. CEHTF staff, despite being aware, offered no effective means to reduce gender inequity involving cataract surgical services. Interventions that successfully increased service utilization by girls came from individual cases, involving extraordinary effort by a single eye care programme person. CONCLUSION: Community-based case finders such as Anganwadi workers in India, Female Community Health Volunteers (FCHVs) in Nepal, and Key Informants (KIs) in Africa are necessary to identify children in need of cataract services, but insufficient to increase service utilization by girls. Secondary, often extra-ordinary community-based interventions by eye care personnel are needed in all settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".