Improving the Follow-up Rate for Pediatric Patients (0-16 years) of an Eye Hospital in Nepal: Protocol for a Public Health Intervention Study
Bibliographic record
Abstract
BACKGROUND: The follow-up of pediatric patients ensures regular ocular morbidity monitoring and better treatment outcome. Hiralal Santudevi Pradhan Institute of Ophthalmic Science (Bharatpur Eye Hospital [BEH]) noticed that the follow-up rate was only 22% among its pediatric patients. Several factors like lack of awareness and forgetfulness among patients may contribute to a lower number of follow-up visits. Therefore, BEH decided to find if counseling and reminders through SMS text messaging and phone calls would improve the follow-up rates. OBJECTIVE: This study aims to evaluate the impact of interventions like counseling and reminder SMS text messaging and phone calls in improving the follow-up rate of pediatric patients. METHODS: This is a public health intervention study being conducted using quantitative analysis. All children (0-16 years) with ocular conditions requiring at least 3 follow-up visits in the study period will be included. In all, 264 participants will be allocated to 3 groups: routine standard care, counseling, and reminders with SMS text messaging and phone calls. In counseling, patients will take part in 20-minute counseling sessions with trained counselors at each visit, and information leaflets will be provided to them. In the reminder SMS text messaging and phone call group, patients will receive an SMS text message 3 days prior and a phone call 1 day prior to their scheduled visits. Patients attending within 2 days of the scheduled date will be considered compliant to follow-up. The proportion of patients completing all the follow-up visits in each group will be assessed. Informed consent will be taken from parents and children. Univariate and multivariate analyses will be conducted. RESULTS: The ethical approval for this study has been obtained from the Ethical Review Board (ERB) of Nepal Health Research Council (ERB protocol registration #761/2020 P). The data collection was initiated on January, 24, 2021, but due to the COVID-19 pandemic, as of September 2021, we have only been able to enroll 154 of the planned 264 participants (58.3% of the sample size). CONCLUSIONS: This study will reliably document not only the factors associated with follow-up rate through an intervention package (counseling and reminders through SMS text messaging and phone calls) but also the cost effectiveness of the intervention package, which can be applied in all the departments of the hospital. TRIAL REGISTRATION: ClinicalTrials.gov NCT04837534; https://clinicaltrials.gov/ct2/show/NCT04837534. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31578.
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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 | 0.036 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".