Interventions to Counter Health Misinformation Among Older People: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: In contemporary society, misinformation and disinformation have emerged as significant challenges, impacting various aspects of public health and societal cohesion. Some authors argue that older adults are particularly vulnerable to the effects of misinformation due to potential digital health literacy challenges. A previous review identified pedagogical approaches most commonly adopted in interventions aiming to improve the digital literacy of older adults but did not specifically address digital health literacy. OBJECTIVE: This scoping review protocol aims to explore digital health literacy interventions targeting health misinformation and designed specifically for older adults. METHODS: Following the methodology outlined by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist, this protocol delineates a systematic approach encompassing 5 stages: identification of research questions, identification of relevant studies, selection of studies, data charting, and collation of findings. Our scoping review will include peer-reviewed literature on interventions targeting misinformation for older adults. Research will be conducted on the MEDLINE (Ovid), Embase (Elsevier), PsycINFO (Ovid), CINAHL, and Web of Science databases. Gray literature will also be surveyed by performing a Google search to identify interventions and tools employed by public or private organizations, institutes, groups, or agencies. The databases and gray literature will be searched to identify relevant publications. Two members of our team will independently select publications to include in the review by using the Covidence review software (Veritas Health Innovation). The publications included will specifically address our research questions, be peer-reviewed, evidence-based, and published from January 1, 2005, in full-text English or French version. Data will be extracted from the included publications to mainly chart the intervention's objectives, types, target age groups, effectiveness, and risks reported. A thematic analysis will be conducted to categorize the study findings. RESULTS: The funding for this project was provided in March 2024. The research questions were identified in January 2024. The databases and gray literature search strategies were developed in February 2024. The final selection of the publications; the charting, collating, and summarizing of data; along with the reporting of findings are planned for August to September 2025. The findings of this scoping review will be shared through publication in an open access journal and presentations scheduled between September and December 2025. CONCLUSIONS: This protocol will enable us to contribute to the advancement of knowledge in combating health misinformation among older adults. The results will also be utilized for the development of interventions targeting misinformation among older adults. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/74138.
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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 | 0.094 | 0.093 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.136 | 0.024 |
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".