A Patient-Centered Asthma Management Communication Intervention for Rural Latino Children: Protocol for a Waiting-List Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Rural Latino children with asthma suffer high rates of uncontrolled asthma symptoms, emergency department visits, and repeat hospitalizations. This vulnerable population must negotiate micro- and macrolevel challenges that impact asthma management, including language barriers, primary care access, parental time off from work, insurance coverage, distance from specialty sites, and documentation status. There are few proven interventions that address asthma management embedded within this unique context. OBJECTIVE: Using a bio-ecological approach, we will determine the feasibility of a patient-centered collaborative program between rural Latino children with asthma and their families, school-based nursing programs, and primary care providers, facilitated by the use of a smartphone-based mobile app with a Spanish-language interface. We hypothesize that improving communication through a collaborative, patient-centered intervention will improve asthma management, empower the patient and family, decrease outcome disparities, and decrease direct and indirect costs. METHODS: The specific aims of this study include the following: (1) Aim 1: produce and validate a Spanish translation of an existing asthma management app and evaluate its usability with Latino parents of children with asthma, (2) Aim 2: develop and evaluate a triadic, patient-centered asthma intervention preliminary protocol, facilitated by the bilingual mobile app validated in Aim 1, and (3) Aim 3: investigate the feasibility of the patient-centered asthma intervention from Aim 2 using a waiting-list randomized controlled trial (RCT) to investigate the effects of the intervention on school days missed and medication adherence. RESULTS: Mobile app translation, initial usability testing, and app software refinement were completed in 2019. Analysis is in progress. Preliminary protocol testing is underway; we anticipate that the waiting-list RCT, using the refined protocol developed in Aim 2, will commence in fall 2020. CONCLUSIONS: Tailored, technology-based solutions have the potential to successfully address issues affecting asthma management, including communication barriers, accessibility issues, medication adherence, and suboptimal technological interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04633018; https://www.clinicaltrials.gov/ct2/show/NCT04633018. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18977.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.079 | 0.011 |
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".