Improving Access to Specialty Care for Rural Children Using Enhanced Hearing Screening and Specialty Telehealth Follow-Up in Rural Kentucky Schools: Protocol for a Hybrid Effectiveness-Implementation Stepped Wedge, Cluster-Randomized Controlled Trial (Appalachian STAR Trial)
Bibliographic record
Abstract
BACKGROUND: Rural populations are disproportionately affected by preventable childhood hearing loss, which is associated with speech and language delays, impaired social development, and decreased educational attainment. Rural schools are critical access points for preventive health screenings such as hearing screening, but variable screening implementation, loss to follow-up, and scarcity of specialists in rural areas diminish program effectiveness. We seek to implement a school-based telehealth intervention to increase access to specialty hearing care for children in rural Kentucky. OBJECTIVE: The Appalachian Specialty Telemedicine Access for Referrals (STAR) trial will assess effectiveness and implementation of the novel, evidence-based STAR model, consisting of 3 core components: (1) enhanced hearing screening; (2) specialty telehealth follow-up; and (3) streamlined communication between schools, health care providers, and parents and caregivers. METHODS: Adaptation of the STAR model for rural Kentucky will occur in the first 2 years, followed by a phased rollout of the intervention using a stepped wedge, cluster-randomized design among kindergartners enrolled in approximately 63 schools in 14 counties of rural Kentucky. School districts were identified based on scientific and community input, as well as geographic proximity to state-run clinics, which provide audiology evaluation free of charge. School districts were randomized into 2 sequences using constrained randomization to balance baseline covariates, such as kindergarten enrollment and number screened. This hybrid type 1 effectiveness-implementation trial will evaluate effectiveness of the STAR model compared with usual hearing screening and usual follow-up process using an intention-to-treat approach with generalized estimating equations. Barriers and facilitators to implementation of the intervention will be identified using a mixed methods approach. The primary effectiveness outcomes are the (1) proportion of kindergarteners screened and (2) proportion of referred kindergarteners who receive specialty follow-up within 60 days of screening. Implementation outcomes include assessment of factors affecting successful integration of the STAR model. Iterative adaptation of the intervention will be performed at prespecified time points to maximize implementation outcomes. RESULTS: The trial began in September 2022 and is expected to conclude in May 2026. Final data analysis is planned to begin in June 2026, and publication of results is expected in 2027. CONCLUSIONS: The STAR model addresses issues related to identification of hearing loss, loss to follow-up from screening, and access to specialty care in rural Kentucky. Effectiveness outcomes may inform future policy for school hearing screening, including adoption of evidence-based protocols to address preventable childhood hearing loss and integration of school-based specialty telehealth follow-up to improve follow-up. Implementation aims may maximize the STAR model's adaptability and overall fit. Community input and systematic adaptation will ensure consideration of unique needs and priorities of rural Kentucky counties. If successful, the STAR model could be scaled across rural America and applied to other preventable child health conditions. TRIAL REGISTRATION: ClinicalTrials.gov NCT05513833; https://clinicaltrials.gov/study/NCT05513833. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77630.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".