Using YouTube to Disseminate Effective Vaccination Pain Treatment for Babies
Bibliographic record
Abstract
BACKGROUND: Infant vaccinations are necessary for public health, but are painful, causing distress to the infant and caregivers. Breastfeeding and sucrose effectively reduce infants' pain during vaccinations, and these strategies are recommended in health care provider (HCP)-targeted education and vaccination pain guidelines. However studies show these strategies are infrequently used. YouTube is a popular medium to publicly share and watch videos, and many consumer posted YouTube videos show distressed infants being vaccinated with no pain treatment. The aims of this study were to evaluate the reach and impact of a consumer-targeted YouTube video demonstrating use of effective pain reduction strategies during infant vaccinations. METHODS: A brief consumer-targeted video showing two infants being vaccinated was posted onto YouTube on October 2013. One infant was breastfed and another infant received sucrose by mouth before and during the injection. A link to a viewer survey was visible on a banner near the end of the video. An intensive strategically planned knowledge dissemination strategy using the media, social media and messages to professional organizations took place to promote the video. Data analysis of the viewer survey, YouTube analytics of the reach of the video in terms of number of views, country of viewers, and comments relating to the video took place 12 months after the video was posted. RESULTS: Twelve months after posting, the video had 65,478views, 68 comments, 245 likes, 17 dislikes, and 90 shares. Average duration of viewer time was 65% of the video. The viewer survey was completed by 156 (0.24%) viewers; 90 (58%) answered as HCPs and 66 (42%) as parents. Survey results showed that the video was persuasive; intent to use or support breastfeeding or sucrose was high in both parents and HCPs after viewing the video. Comments posted were often emotional in nature, and were related to anti-vaccination (n = 26, 38%); effectiveness or positive personal experiences (n = 21, 32%); research team comments or promotion (n = 12, 18%); pro-vaccination (n = 6, 8%) and barriers to using breastfeeding or sucrose during vaccinations (n = 3, 4%). CONCLUSION: The video posted onto YouTube demonstrating effective pain treatment during infant vaccinations was viewed by large numbers of people around the world, however the response rate to the linked survey was extremely low. Using YouTube videos for knowledge dissemination has an extensive reach, however it is difficult to evaluate impact on behaviours and practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".