Use of equity-informed social media COVID-19 risk communication tools: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Health agencies and community organisations play a crucial role in disseminating information to the public about COVID-19 risks and events, providing instructions on how to change behaviour to mitigate those risks, motivating compliance with health directives and addressing false information. Social media platforms are a critical tool in risk communication, providing a medium for rapid transmission of messages as well as providing the opportunity for engagement and immediate feedback. Access to health information, services and support are especially important for marginalised and underserved ('equity-deserving') populations who are disproportionately affected by COVID-19. This scoping review aims to review the breadth and depth of the academic and grey literature on equity-informed social media risk communication tools to provide guidance on promising practices and principles for reaching equity-deserving populations through social media. METHODS AND ANALYSIS: Arksey and O'Malley's (2005) framework guided the identification of the research question; identification and selection of relevant studies from electronic databases and hand-searches of discipline-specific journals; extraction and charting of the data; and collating and reporting of findings. The results of the screening process will be reported using the Preferred Reporting Items for Systematic Review and Meta-Analysis-Scoping Review guidelines. FINDINGS: We will identify reported facilitators and barriers to the development of risk communications that target equity-deserving communities. We will also identify recommendations for equity-informed risk communication for COVID-19. ETHICS AND DISSEMINATION: This study does not require ethics approval. We intend to disseminate the results through publication in an open-access peer-reviewed journal, conference presentations, lay summaries (eg, checklists) for health organisations and messages to be shared through social media.
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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 | 0.166 | 0.156 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.018 |
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".