Use of Implementation Science Concepts in the Study of Misinformation: A Scoping Review
Bibliographic record
Abstract
Misinformation hinders the impact of public health initiatives. Efforts to counter misinformation likely do not consider the full range of factors known to affect how individuals make decisions and act on them. Implementation science tools and concepts can facilitate the development of more effective interventions against health misinformation by leveraging advances in behavior specification, uptake of evidence, and theory-guided development and evaluation of complex interventions. We conducted a scoping review of misinformation literature reviews to document whether and how important concepts from implementation science have already informed the study of misinformation. Of 90 included reviews, the most frequently identified implementation science concepts were consideration of mechanisms driving misinformation (78%) and ways to intervene on, reduce, avoid, or circumvent it (71%). Other implementation science concepts were discussed much less frequently, such as tailoring strategies to the relevant context (9%) or public involvement in intervention development (9%). Less than half of reviews (47%) were guided by any theory, model, or framework. Among the 26 reviews that cited existing theories, most used theory narratively (62%) or only mentioned/cited the theory (19%), rather than using theory explicitly to interpret results (15%) or to inform data extraction (12%). Despite considerable research and many summaries of how to intervene against health misinformation, there has been relatively little consideration of many important advances in the science of health care implementation. This review identifies key areas from implementation science that might be useful to support future research into designing effective misinformation interventions.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".