Public Health Messaging About Dengue on Facebook in Singapore During the COVID-19 Pandemic: Content Analysis
Bibliographic record
Abstract
BACKGROUND: Dengue, a mosquito-borne disease, has been a health challenge in Singapore for decades. In 2020, during the COVID-19 pandemic, Singapore encountered a serious dengue outbreak and deployed various communication strategies to raise public awareness and mitigate dengue transmission. OBJECTIVE: Drawing on the Crisis and Emergency Risk Communication (CERC) framework, this study examines how dengue-related messages communicated on Facebook (Meta) during the COVID-19 pandemic fall into the CERC themes. This study also seeks to understand how these themes differ between dengue outbreak (eg, 2020) and nonoutbreak years (eg, 2021). In addition, we explore how message themes on dengue changed across different CERC phases within the dengue outbreak year. METHODS: We conducted a content analysis on 314 Facebook posts published by public health authorities in Singapore between January 1, 2020, and September 30, 2022. We conducted chi-square tests to examine the differences in message themes between the dengue outbreak and nonoutbreak years. We also conducted chi-square tests to examine how these message themes varied across 3 CERC phases during the dengue outbreak year. RESULTS: Our findings suggest that during the dual epidemics of dengue and COVID-19, Singapore's public health communication on dengue largely adhered to CERC principles. Dengue-related messaging, particularly regarding intelligence and requests for contributions, significantly varied between outbreak and nonoutbreak years. In addition, messages on general advisories and vigilance, as well as those on social and common responsibility, significantly differed across the CERC phases during the dengue outbreak year. CONCLUSIONS: Singapore's public health authorities flexibly adjusted their messaging strategies on social media platforms in response to the evolving dengue situation during the COVID-19 pandemic, demonstrating the high adaptability of the government's health communication amid the dual epidemics. However, several areas for improvement should also be noted for future public health communication to mitigate dengue transmission.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".