Interventions to improve gender equity in eye care in low-middle income countries: A systematic review
Bibliographic record
Abstract
PURPOSE: Women bear an inequitable burden of blinding conditions compared to men primarily because they have more limited access to eye care services. This systematic review sought evidence regarding interventions to increase gender equity in eye care. METHODS: We searched MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, and EBSCO CINAHL, and contacted experts to identify studies in low- and middle-income countries of health services interventions for age-related cataract, childhood cataract, and trachoma. Eligible studies could be clinical trials or observational studies, but had to present sufficient data for intervention effects to be estimated separately for women and men. RESULTS: We included four cluster RCTs and nine observational studies. All were judged to have serious risk of bias. Six studies examined interventions involving training rural community volunteers to identify, educate and assist individuals with unmet eye care needs. Interventions were associated with reduced gender inequities in all-cause blindness, clinic attendance, cataract surgery coverage and trachoma treatment coverage (low-to-very low quality evidence). Studies in Nepal and Tanzania examining a multicomponent intervention to improve follow-up after pediatric cataract surgery found reduced gender inequities in follow-up rates at 10 weeks (low quality evidence). CONCLUSION: Limited evidence exists to inform health service planners regarding interventions to reduce gender inequity in visual impairment and blindness. Training community volunteers to identify and counsel affected individuals, and empower them to circumvent or challenge socioeconomic barriers to accessing care holds promise. Future interventions ought to explicitly consider gender in their design and implementation, and incorporate high-quality evaluation efforts.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".