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Record W4225143654 · doi:10.2196/36043

A 3-Item Measure of Digital Health Care Literacy: Development and Validation Study

2022· article· en· W4225143654 on OpenAlexvenueno aff
Lyndsay A. Nelson, Jacquelyn S. Pennings, Evan C. Sommer, Filoteia Popescu, Shari L. Barkin

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Heart, Lung, and Blood InstituteNational Institutes of HealthVanderbilt University Medical CenterVanderbilt University
KeywordsCronbach's alphaExploratory factor analysisConfirmatory factor analysisScale (ratio)Health literacyHealth carePsychologyReliability (semiconductor)TelehealthLiteracyConstruct validityMedicinePsychometricsFamily medicineTelemedicineClinical psychologyStatisticsStructural equation modeling

Abstract

fetched live from OpenAlex

BACKGROUND: With increased reliance on digital health care, including telehealth, efficient and effective ways are needed to assess patients' comfort and confidence with using these services. OBJECTIVE: The goal of this study was to develop and validate a brief scale that assesses digital health care literacy. METHODS: We first developed an item pool using existing literature and expert review. We then administered the items to participants as part of a larger study. Participants were caregivers of children receiving care at a pediatric clinic who completed a survey either on the web or over the telephone. We randomized participants into development and confirmatory samples, stratifying by language so that exploratory factor analysis and confirmatory factor analysis could be performed with separate samples of participants. We assessed the scale's validity by examining its associations with participants' demographics, digital access, and prior digital health care use. RESULTS: Participants (N=508) were, on average, aged 34.7 (SD 7.7) years, and 89.4% (454/508) were women. Of the 508 participants, 280 (55.1%) preferred English as their primary language, 157 (30.9%) preferred Spanish, and 71 (14%) preferred Arabic; 228 (45%) had a high school degree or less; and 230 (45.3%) had an annual household income of <US $35,000. Using exploratory factor analysis, 3 items were retained in a reduced scale with excellent reliability (Cronbach α=.90) and a high variance explained (78%). The reduced scale had excellent fit, with factor loadings between 0.82 and 0.94. All fit statistics exceeded the criteria for good fit between the proposed factor structure and the data. We refer to this scale as the Digital Health Care Literacy Scale. The scale was positively associated with education (ρ=0.139; P=.005) and income (ρ=0.379; P<.001). Arabic speakers had lower scores than English (P<.001) and Spanish speakers (P=.02), and Spanish speakers had lower scores than English speakers (P<.001). Participants who did not own a smartphone (P=.13) or laptop computer (P<.001) had lower scores than those who owned these devices. Finally, participants who had not used digital tools, including health apps (P<.001) and video telehealth (P<.001), had lower scores than those who had used these tools. CONCLUSIONS: Despite the potential for digital health care to improve quality of life and clinical outcomes, many individuals may not have the skills to engage with and benefit from it. Moreover, these individuals may be those who already experience worse outcomes. A screening tool such as the Digital Health Care Literacy Scale could be a useful resource to identify patients who require additional assistance to use digital health services and help ensure health equity.

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)0.009
metaresearch head score (Gemma)0.013
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: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.156
GPT teacher head0.558
Teacher spread0.403 · 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
GenreMethods

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

Citations103
Published2022
Admission routes1
Has abstractyes

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