Complementary, alternative, and integrative medicine-specific COVID-19 misinformation on social media: A scoping review
Bibliographic record
Abstract
Background: The sharing of health-related information has become increasingly popular on social media. Unregulated information sharing has led to the spread of misinformation, especially regarding complementary, alternative, and integrative medicine (CAIM). This scoping review synthesized evidence surrounding the spread of CAIM-related misinformation on social media during the COVID-19 pandemic. Methods: This review was informed by a modified version of the Arksey and O'Malley scoping review framework. AMED, EMBASE, PsycINFO and MEDLINE databases were searched systematically from inception to January 2022. Eligible articles explored COVID-19 misinformation on social media and contained sufficient information on CAIM therapies. Common themes were identified using an inductive thematic analysis approach. Results: Twenty-eight articles were included. The following themes were synthesized: 1) misinformation prompts unsafe and harmful behaviours, 2) misinformation can be separated into different categories, 3) individuals are capable of identifying and refuting CAIM misinformation, and 4) studies argue governments and social media companies have a responsibility to resolve the spread of COVID-19 misinformation. Conclusions: Misinformation can spread more easily when shared on social media. Our review suggests that misinformation about COVID-19 related to CAIM that is disseminated online contributes to unsafe health behaviours, however, this may be remedied via public education initiatives and stricter media guidelines. The results of this scoping review are crucial to understanding the behavioural impacts of the spread of COVID-19 misinformation about CAIM therapies, and can inform the development of public health policies to mitigate these issues.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".