Analysis of Informative Content on Cerebral Palsy Presented in Brazilian-Portuguese YouTube Videos
Bibliographic record
Abstract
Aims: To describe the characteristics of the most accessed YouTube videos in Brazilian-Portuguese on cerebral palsy (CP), and to analyze content of informational videos about this topic.Methods: This was a cross-sectional study. Searching on YouTube website was conducted by two independent examiners between November and December 2019, using the keywords “Paralisia Cerebral” sorted by videos’ number of views. Videos that did not present content related to CP or duplicate videos were excluded. The interaction parameters and content characteristics of the included videos were extracted. To access the trustworthiness and quality of informational videos, the modified Discern checklist and the Global Quality Score was used.Results: Following the eligibility criteria 90 videos were included. Fifty-three (53) were classified as experiential videos and 37 as informational videos. Informational videos presented multi-topics about different aspects of CP. This group of videos presented moderate trustworthiness due to the lack of scientific evidence content. Informational videos had good quality and generally good flow.Conclusion: YouTube presented a large number of videos about CP in Brazilian-Portuguese. Informational videos are useful for patients and healthcare providers; however, it is necessary to included information about scientific evidence, as a strategy to facilitate and promote knowledge translation.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".