Alternative community-led intervention to improve uptake of cataract surgery services in rural Tanzania—The Dodoma Community Cataract Acceptance Trial (DoCCAT): a protocol for intervention co-designing and implementation in a cluster-randomized controlled trial
Bibliographic record
Abstract
Age-related lens opacification (cataract) remains the leading cause of visual impairment and blindness worldwide. In low- and middle-income countries, utilization of cataract surgical services is often limited despite community-based outreach programmes. Community-led research, whereby researchers and community members collaboratively co-design intervention is an approach that ensures the interventions are locally relevant and that their implementation is feasible and socially accepted in the targeted contexts. Community-led interventions have the potential to increase cataract surgery uptake if done appropriately. In this study, once the intervention is co-designed it will be implemented through a cluster-randomized controlled trial (cRCT) with ward as a unit of randomization. This study will utilise both the qualitative methods for co-designing the intervention and the quantitative methods for effective assessment of the developed community-led intervention through a cRCT in 80 rural wards of Dodoma region, Tanzania (40 Intervention). The 'intervention package' will be developed through participatory community meetings and ongoing evaluation and modification of the intervention based on its impact on service utilization. Leask's four stages of intervention co-creation will guide the development within Rifkin's CHOICE framework. The primary outcomes are two: the number of patients attending eye disease screening camps, and the number of patients accepting cataract surgery. NVivo version 12 will be used for qualitative data analysis and Stata version 12 for quantitative data. Independent and paired t-tests will be performed to make comparisons between and within groups. P-values less than 0.05 will be considered statistically significant.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".