The Role of YouTube and the Entertainment Industry in Saving Lives by Educating and Mobilizing the Public to Adopt Behaviors for Community Mitigation of COVID-19: Successive Sampling Design Study
Bibliographic record
Abstract
BACKGROUND: Effective community mitigation through voluntary behavior change is currently the best way to reduce mortality caused by coronavirus disease (COVID-19). This study builds on our prior study based on the scientific premise that YouTube is one of the most effective ways to communicate and mobilize the public in community mitigation to reduce exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: Because of the rapidly changing nature of YouTube in the context of the COVID-19 pandemic, we conducted a follow-up study to document how coverage of preventive behaviors for effective community mitigation has changed. METHODS: A successive sampling design was used to compare coverage of behaviors to mitigate community transmission of COVID-19 in the 100 most widely viewed YouTube videos in January 2020 and March 2020. RESULTS: Videos in the January and March samples were viewed >125 million times and >355 million times, respectively. Fewer than half of the videos in either sample covered any of the prevention behaviors recommended by the US Centers for Disease Control and Prevention, but many covered key prevention behaviors and were very widely viewed. There were no videos uploaded by entertainment television in the January sample, but this source comprised the majority of videos and garnered the majority of cumulative views in the March sample. CONCLUSIONS: This study demonstrates the incredible reach of YouTube and the potential value of partnership with the entertainment industry for communicating and mobilizing the public about community mitigation to reduce mortality from the COVID-19 viral pandemic.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".