The Effectiveness of eHealth Interventions in Promoting the Health Literacy of Informal Caregivers: A Systematic Literature Review Protocol
Bibliographic record
Abstract
BACKGROUND: Digital Information and Communication Technologies (ICTs) offer significant opportunities to enhance public health, particularly through their strategic use in promoting health literacy. OBJECTIVES: This systematic review protocol aims to outline the methodological steps necessary to conduct a systematic review of the effectiveness of digital interventions in improving health literacy among informal caregivers. METHODS: This review will include studies involving adult informal caregivers (≥18 years) undergoing a digital health intervention promoting health literacy. Intervention, effectiveness or efficacy, RCT, quasi-experimental, and observational studies will be eligible. This review will follow the methodology for Cochrane Systematic Review and Meta-Analysis. The search strategy will identify studies published in the databases SCOPUS and Web of Science, as well as CINAHL (via EBSCO) and PubMed and gray literature sources. Two in-dependent reviewers will screen the studies, extract data, and critically appraise the selected studies. It is planned that the risk of bias will be assessed using the RoB2, Newcastle-Ottawa Scale (NOS), and the ROBINS-I (Risk Of Bias In Non-randomized Studies of Interventions). From the included studies, data will be extracted on the identification of the study, the aim of the study, characteristics of the population, study method and intervention and control conditions, study variables, and significant results. CONCLUSIONS: It is anticipated that the results of the study will guide healthcare professionals and managers in incorporating digital technologies into health literacy programs for informal caregivers. Whenever possible, a statistical meta-analysis will be performed to combine study results. The PROSPERO registration number is CRD42024589465.
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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 | 0.103 | 0.107 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.010 |
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".