Performance-Based Measurement of eHealth Literacy: Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: eHealth literacy describes the ability to locate, comprehend, evaluate, and apply web-based health information to a health problem. In studies of eHealth literacy, researchers have primarily assessed participants' perceived eHealth literacy using a short self-report instrument, for which ample research has shown little to no association with actual performed eHealth-related skills. Performance-based measures of eHealth literacy may be more effective at assessing actual eHealth skills, yet such measures seem to be scarcer in the literature. OBJECTIVE: The primary purpose of this study was to identify tools that currently exist to measure eHealth literacy based on objective performance. A secondary purpose of this study was to characterize the prevalence of performance-based measurement of eHealth literacy in the literature compared with subjective measurement. METHODS: We conducted a systematic scoping review of the literature, aligning with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, in 3 stages: conducting the search, screening articles, and extracting data into a summary table. The summary table includes terminology for eHealth literacy, description of participants, instrument design, health topics used, and a brief note on the evidence of validity for each performance-based measurement tool. A total of 1444 unique articles retrieved from 6 relevant databases (MEDLINE; PsycINFO; CINAHL; Library and Information Science Abstracts [LISA]; Library, Information Science & Technology Abstracts [LISTA]; and Education Resources Information Center [ERIC]) were considered for inclusion, of which 313 (21.68%) included a measure of eHealth literacy. RESULTS: Among the 313 articles that included a measure of eHealth literacy, we identified 33 (10.5%) that reported on 29 unique performance-based eHealth literacy measurement tools. The types of tools ranged from having participants answer health-related questions using the internet, having participants engage in simulated internet tasks, and having participants evaluate website quality to quizzing participants on their knowledge of health and the web-based health information-seeking process. In addition, among the 313 articles, we identified 280 (89.5%) that measured eHealth literacy using only a self-rating tool. CONCLUSIONS: This study is the first research synthesis looking specifically at performance-based measures of eHealth literacy and may direct researchers toward existing performance-based measurement tools to be applied in future projects. We discuss some of the key benefits and drawbacks of different approaches to performance-based measurement of eHealth literacy. Researchers with an interest in gauging participants' actual eHealth literacy (as opposed to perceived eHealth literacy) should make efforts to incorporate tools such as those identified in this systematic scoping review.
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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 | 0.048 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".