Exploring Service Users’ Experiences of a Community-Based Intervention to Improve Follow-Up at Bharatpur Eye Hospital in Nepal: Qualitative Study
Bibliographic record
Abstract
Background: Follow-up to eye care services for children, especially in the context of Nepal, is essential for ensuring a continuum of care. Hence, as a continued effort, we designed this study to explore the service users' experience of a community-based intervention to improve follow-up at Bharatpur Eye Hospital (BEH) in Nepal. Objective: This study aimed to explore service users' experiences and perceptions of the community-based follow-up intervention for eye care services implemented by BEH in Nepal. Methods: A qualitative study using an in-depth interview approach was used. Participants were purposively selected for this research study. Participants who were part of a quasi-experimental study conducted to improve follow-up services in BEH and their service catchment area were chosen. Participants who had not attended even a single follow-up visit and participants who attended at least one follow-up were recruited for this qualitative study. Based on the distance from the base hospital and the follow-up status, 65 participants were initially found eligible for the qualitative study. However, only 17 participants were available and consented. Topic guides were developed for the purpose of in-depth interviews specifically for participants who had not attended even the first follow-up visits and those who attended at least one follow-up visit. A total of 2 ophthalmic assistants, who were not a part of the main intervention study, conducted the interviews. Results: In total, 17 service users whose children were receiving services from BEH participated in the qualitative study. We identified 4 key themes that provided invaluable information about the barriers and facilitators to follow-up as well as the experiences (positive or negative) of the participants that need to be considered in any future initiatives to improve follow-up in Nepal. Conclusions: This study highlights the need for systematic development of interventions to address the unmet need for eye care services in the community through innovative, scalable solutions. As a next step, the BEH team will be working to develop such scalable solutions for Nepal. Such interventions will also need to be optimized for similar settings and countries to meet the goals of universal health coverage, vision 2030, and sustainable development goals worldwide.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".