Experiences of Governments and Public Health Agencies Regarding Crisis Communication During the COVID-19 Pandemic in the Digital Age: Protocol for a Systematic Review of Qualitative Studies
Bibliographic record
Abstract
BACKGROUND: Governments and public health agencies worldwide experienced difficulties with social media-mediated infodemics on the internet during the COVID-19 pandemic. Existing public health crisis communication strategies need to be updated. However, crisis communication experiences of governments and public health agencies worldwide during the COVID-19 pandemic have not been systematically compiled, necessitating updated crisis communication strategies. OBJECTIVE: This systematic review aims to collect and organize the crisis communication experiences of senders (ie, governments and public health agencies) during the COVID-19 pandemic. Our focus is on exploring the difficulties that governments and public health agencies experienced, best practices in crisis communication by governments and public health agencies during the COVID-19 pandemic in times of infodemic, and challenges that should be overcome in future public health crises. METHODS: We plan to begin the literature search on May 1, 2024. We will search PubMed, MEDLINE, CINAHL, PsycINFO, PsycARTICLES, Communication Abstracts, and Web of Science. We will filter our database searches to search from the year 2020 and beyond. We will use a combination of keywords by referring to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, and Research type) tool to search the abstracts in databases. We intend to include qualitative studies on crisis communication by governments and public health agencies (eg, officials, staff, health professionals, and researchers) to the public. Quantitative data-based studies will be excluded. Only papers written in English will be included. Data on study characteristics, study aim, participant characteristics, methodology, theoretical framework, object of crisis communication, and key results will be extracted. The methodological quality of eligible studies will be assessed using the Joanna Briggs Institute critical appraisal checklist for qualitative research. A total of 2 independent reviewers will share responsibility for screening publications, data extraction, and quality assessment. Disagreement will be resolved through discussion, and the third reviewer will be consulted, if necessary. The findings will be summarized in a table and a conceptual diagram and synthesized in a descriptive and narrative review. RESULTS: The results will be systematically integrated and presented in a way that corresponds to our research objectives and interests. We expect the results of this review to be submitted for publication by the end of 2024. CONCLUSIONS: To our knowledge, this will be the first systematic review of the experiences of governments and public health agencies regarding their crisis communication to the public during the COVID-19 pandemic. This review will contribute to the future improvement of the guidelines for crisis communication by governments and public health agencies to the public. TRIAL REGISTRATION: PROSPERO CRD42024528975; https://tinyurl.com/4fjmd8te. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/58040.
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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 | 0.188 | 0.167 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".