Using operational research as a tool to improve eye health services and systems in low-and middle-income settings: lessons from India and Nepal
Bibliographic record
Abstract
BACKGROUND: Operational Research (OR), as part of a quality assurance program, has become a standard feature of most health institutions in most high-income countries. In contrast, in low-income settings, operational research is less common, and almost no one has asssed operational research capacity building (ORCB) as a tool to improve efficacy, efficiency and quality in these settings. This study evaluated the impact of an ORCB program on participants' research competencies and the extent to which research findings were implemented in practice. MATERIALS AND METHODS: This study combined quantitative and qualitative data to evaluate an ORCB intervention in eye hospitals in Nepal (3 sites) and northern India (1 site) from 2019 to 2022. A self-reported questionnaire was administered at the end of the study period, and formal interviews were conducted. The questionnaire covered knowledge improvement, practice implementation, and motivating and challenging factors. Statistical analysis included paired t-tests to compare pre- and post-training scores. Qualitative data were gathered through interviews and observations and analysed thematically. RESULTS: The program demonstrated significant improvements in participants' research knowledge gain. Quantitative analysis revealed substantial gains in knowledge (p-values < 0.05 for all domains). Post-training, 66.7% developed study protocols, and 60% trained other staff or students. Qualitative feedback indicated overall positive impacts, including enhanced research and operational activities. However, reported challenges such as inconsistent mentorship quality, poor internet connectivity during online sessions, and difficulty in balancing clinical work with research. Despite these challenges, there was notable improvement in research practice and internal training within hospitals, and the program's approach was appreciated for its effectiveness. CONCLUSION: The study highlights the need for standardized training modules, consistent mentorship, and stronger institutional support. Building operational research capacity in resource-poor settings with limited administrative staff and weak data infrastructure improves individual staff knowledge and skills. Participants learned about scientific principles of reliability and validity and their importance to efforts to improve service equity, efficiency, and effectiveness.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".