COVID-19 health misinformation: using design-based research to develop a theoretical framework for intervention
Bibliographic record
Abstract
Purpose Because health misinformation pertaining to COVID-19 is a serious threat to public health, the purpose of this study is to develop a framework to guide an online intervention into some of the drivers of health misinformation online. This framework can be iterated upon through the use of design-based research to continue to develop further interventions as needed. Design/methodology/approach Using design-based research methods, in this paper, the authors develop a theoretical framework for addressing COVID-19 misinformation. Using a heuristic analysis of research on vaccine misinformation and hesitancy, the authors propose a framework for education interventions that use the narrative effect of transportation as a means to increase knowledge of the drivers of misinformation online. Findings This heuristic analysis determined that a key element of narrative transportation includes orientation towards particular audiences. Research indicates that mothers are the most significant household decision-makers with respect to vaccines and family health in general; the authors suggest narrative interventions should be tailored specifically to meet their interests and tastes, and that this may be different for mothers of different backgrounds and cultural communities. Originality/value While there is a significant body of literature on vaccine hesitancy and vaccine misinformation, more research is needed that helps people understand the ways in which misinformation works upon social media users. The framework developed in this research guided the development of an education intervention meant to facilitate this understanding.
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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 | 0.171 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".