Digital Technology Characteristics and Literacy Among Families With Children With Asthma: Cross-Sectional Study
Bibliographic record
Abstract
Background: The use of digital technology in pediatric asthma management has emerged as a potential tool for improving asthma management. However, the use of digital tools has the potential to contribute to the inequitable delivery of asthma care because of existing social factors associated with asthma disparities. Our study focused on parents' chosen language and sociodemographic factors that might shape the use of digital technology in asthma self-management. Objective: This study aims to estimate and compare patient, family, and technology-related characteristics by parents' chosen language (English or Spanish) and compare a digital literacy measure by sociodemographic factors. Methods: Survey data were collected from July to December 2021 from parents of children with asthma who were seen by a Chicago pediatric health system pulmonary provider. Questions assessed patient and family characteristics, digital technology use, and digital literacy, measured using the validated eHealth Literacy Scale (eHEALS). Chi-square tests and multivariable logistic regression were used for comparisons, and Kruskal-Wallis tests were used for comparing median eHEALS scores by social characteristics. Results: Of the 197 parents surveyed, 24.4% (n=49) of parents identified as a race categorized as other, 37.1% (n=67) as White, and 38.6% (n=75) as Black; 47.2% (n=93) identified as Hispanic/Latino/Latina. Additionally, 79.7% (n=157) of parents preferred English, and 20.3% (n=40) preferred Spanish. English-speaking parents were more likely to report having a data plan for their smartphone (117/157, 74.5%) or high-speed internet (138/157, 87.9%) compared to Spanish-speaking parents (smartphone: 23/40, 58%; P=.03; internet: 27/40, 68%; P=.002). Compared with Spanish-speaking parents, English-speaking parents were less likely to report having a lot or some concern about paying for internet (28/40, 70% vs 83/157, 52.9%; P=.046) or about data privacy (35/40, 88% vs 105/157, 67.5%; P=.01). Digital literacy scores differed significantly by race, income, education level, and language. In a multivariable model, language was not a significant factor for having high-speed internet service (P=.12) or concern about paying for internet at home (P=.60), but it was a significant factor for concerns about data privacy (P=.04). Conclusions: The significant differences in technology-related characteristics suggest that digital connectivity, affordability, and data privacy may also be important factors in considering digital technology use in asthma care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".