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Record W4380680314 · doi:10.2196/46341

Exploring Caregiver Interest in and Preferences for Interventions for Children With Risk of Asthma Exacerbation: Web-Based Survey

2023· article· en· W4380680314 on OpenAlexvenueno aff
Gabrielle Pogge, David A. Fedele, Erika A. Waters, Julia Maki, Jean Hunleth, Sreekala Prabhakaran, Deborah J. Bowen, James A. Shepperd

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsAsthmaExacerbationPsychological interventionMedicineIntervention (counseling)Asthma exacerbationsFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Maintaining control of asthma symptoms is the cornerstone of asthma treatment guidelines in the United States. However, suboptimal asthma control and asthma exacerbations among young people are common and are associated with many negative outcomes. Interventions to improve asthma control are needed. For such interventions to be successful, it is necessary to understand the types of interventions that are appealing to caregivers of children with different levels of risk of exacerbation. OBJECTIVE: This study aimed to evaluate whether caregivers of children with high (vs low) risk of asthma exacerbation show different levels of interest in and preferences for potential intervention programs and delivery methods. METHODS: We contracted with Ipsos to administer a web-based survey to caregivers of children with asthma who were residing in the United States. Caregivers (N=394) reported their interest (1=not at all; 3=a lot) in 9 possible intervention programs and 8 possible intervention delivery methods. Caregivers also indicated their preferences by selecting the 3 intervention programs and 3 delivery methods that "most" interested them. Finally, caregivers completed 2 open-ended questions asking what other resources might be useful for managing their children's asthma. We classified children as having a high risk of exacerbation if they had an exacerbation in the past 3 months (n=116) and a low risk of exacerbation if otherwise (n=278). RESULTS: Caregivers reported higher levels of interest in all intervention programs and delivery methods if they cared for a child with a high risk rather than a low risk of exacerbation. However, regardless of the child's risk status, caregivers expressed the highest levels of interest in programs to increase their child's self-management skills, to help pay for asthma care, and to work with the school to manage asthma. Caregivers expressed the highest levels of interest in delivery methods that maintained personal control over accessing information (websites, videos, printed materials, and smartphone apps). Caregivers' preferences were consistent with their interests; programs and delivery methods that were rated as high in interest were also selected as one of the 3 that "most" interested them. Although most caregivers did not provide additional suggestions for the open-ended questions, a few caregivers suggested intervention programs and delivery methods that we had not included (eg, education about avoiding triggers and medication reminders). CONCLUSIONS: Similar interests and preferences among caregivers of children with high and low risk of exacerbation suggest a broad need for support in managing childhood asthma. Providers could help caregivers by directing them toward resources that make asthma care more affordable and by helping their children with asthma self-management. Interventions that accommodate caregivers' concerns about having personal control over access to asthma information are likely to be more successful than interventions that do not.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.296
GPT teacher head0.429
Teacher spread0.133 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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