Digital Health Literacy in Bipolar Disorder: International Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: Web-based resources can support people with bipolar disorder (BD) to improve their knowledge and self-management. However, publicly available resources are heterogeneous in terms of their quality and ease of use. Characterizing digital health literacy (the skillset that enable people to navigate and make use of health information in a web-based context) in BD will support the development of educational resources. OBJECTIVE: The aim of this study was to develop understanding of digital health literacy and its predictors in people with BD. METHODS: A web-based survey was used to explore self-reported digital health literacy (as measured by the e-Health Literacy Scale [eHEALS]) in people with BD. Multiple regression analysis was used to evaluate potential predictors, including demographic/clinical characteristics and technology use. RESULTS: A total of 919 respondents (77.9% female; mean age 36.9 years) completed the survey. Older age (β=0.09; P=.01), postgraduate education (β=0.11; P=.01), and current use of self-management apps related to BD (β=0.13; P<.001) were associated with higher eHEALS ratings. CONCLUSIONS: Levels of self-reported digital health literacy were comparable or higher than other studies in the general population and specific physical/mental health conditions. However, individuals with BD who are younger, have completed less education, or are less familiar with mental health apps may require extra support to safely and productively navigate web-based health resources. Relevant educational initiatives are discussed. Future studies should evaluate skill development interventions for less digitally literate groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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; both teacher heads agree on what is shown here.
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".