eHealth literacy measurement tools: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Improving eHealth literacy (eHL) is one of the biggest challenges currently facing the global healthcare community. Indeed the use of digital services has the potential to engage patients in care as well as improve the effectiveness of chronic disease self-management, it remains highly dependent on a patient's specific skills and experiences in the health care systems. Although eHealth literacy has gained momentum in the past decade, it remains an underresearched area, particularly eHealth literacy measurement. The aim of the review is to identify patient-reported outcome measures (PROMs) of eHealth literacy for adult populations and to summarize the evidence on their psychometric properties. METHODS: We will conduct a systematic literature review of the tools used to measure eHealth literacy for adult population. The search strategy aims to find published studies. A three-step search strategy will be used in this review. Published studies will be searched in CINAHL, PubMed, PsycINFO, and Web of Science from inception until end. Grey literature will be searched to find theses. Database search strategies will be formulated and tested with the assistance of an expert Health Sciences Librarian. The selection of studies will be done by two independent reviewers. Disagreements will be resolved through consensus, and a third reviewer will solve discrepancies. Furthermore, two reviewers will independently evaluate the methodological rigor of the instruments development and testing and assign a grade using the standardized Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) checklist. Disagreements will be discussed with a third reviewer, expert in psychometrics. Extracted data will be aggregated and analyzed to produce a set of synthesized findings that will be used to develop evidence-informed recommendations in regard of eHL instruments. We will present a synthesis of all instruments, their psychometric properties, and make recommendations for eHL instrument selection in practice. Reporting will be informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis and a PRISMA flow diagram. DISCUSSION: This systematic review will summarize the evidence on the psychometric properties of PROMs instruments used to measure eHL and will help clinicians, managers, and policy-makers to select an appropriate instrument. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021232765.
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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 | 0.099 | 0.094 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.011 |
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".