Correction: Effect of Door-to-Door Screening and Awareness Generation Activities in the Catchment Areas of Vision Centers on Service Use: Protocol for a Randomized Experimental Study (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED A vision center (VC) is a significant eye care service model to strengthen primary eye care services. VCs have been set up at the block level, covering a population of 150,000-250,000 in rural areas in North India. Inadequate use by rural communities is a major challenge to sustainability of these VCs. This not only reduces the community’s vision improvement potential but also impacts self-sustainability and limits expansion of services in rural areas. The current literature reports a lack of awareness regarding eye diseases and the need for care, social stigmas, low priority being given to eye problems, prevailing gender discrimination, cost, and dependence on caregivers as factors preventing the use of primary eye care. Our organization is planning an awareness-cum-engagement intervention—door-to-door basic eye checkup and visual acuity screening in VCs coverage areas—to connect with the community and improve the rational use of VCs. In this randomized, parallel-group experimental study, we will select 2 VCs each for the intervention arm and the control arm from among poor, low-performing VCs (ie, walk-in of ≤10 patients/day) in our 2 operational regions (Vrindavan, Mathura District, and Mohammadi, Kheri District) of Uttar Pradesh. Intervention will include door-to-door screening and awareness generation in 8-12 villages surrounding the VCs, and control VCs will follow existing practices of awareness generation through community activities and health talks. Data will be collected from each VC for 4 months of intervention. Primary outcomes will be an increase in the number of walk-in patients, spectacle advise and uptake, referral and uptake for cataract and specialty surgery, and operational expenses. Secondary outcomes will be uptake of refraction correction and referrals for cataract and other eye conditions. Differences in the number of walk-in patients, referrals, uptake of services, and cost involved will be analyzed. Background work involved planning of interventions and selection of VCs has been completed. Participant recruitment has begun and is currently in progress. Through this study, we will analyze whether our door-to-door intervention is effective in increasing the number of visits to a VC and, thus, overall sustainability. We will also study the cost-effectiveness of this intervention to recommend its scalability. ClinicalTrials.gov NCT04800718; https://clinicaltrials.gov/ct2/show/NCT04800718
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.198 | 0.017 |
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".