Why are Children Brought Late for Cataract Surgery? Qualitative Findings from Tanzania
Bibliographic record
Abstract
PURPOSE: Worldwide, at least 190,000 children are blind due to cataract. Although, surgical intervention is the treatment of choice, in most developing countries the number of children with cataract being brought to hospital for surgery has been few in number, considerably less than the burden of disease in the community. Furthermore, long delay in presentation is a major deterrent to improved visual outcome and compromises the future quality of life of children and their families. The main objective of this qualitative study was to provide a better understanding of surgical delay in the care of children with congenital or developmental cataract. METHODS: We conducted 117 semi-structured interviews with parents or guardians of children admitted for cataract surgery at a tertiary hospital in northern Tanzania. RESULTS: We identified several factors influencing the treatment-seeking behaviors of parents and guardians, including gender relations within the household, local health beliefs about cataract and cataract surgery and the ability of health care professionals in primary and secondary care settings to adequately inform parents and guardians about cataract and cataract surgery. CONCLUSIONS: Practical, short and medium term avenues must be explored to reduce delays of presentation. Public education, training of health workers and counseling efforts in the communities may be necessary to enable children to access services in a timely fashion.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".