Evaluation of the usefulness and quality of <scp>YouTube™</scp> videos about children's electric toothbrushes
Bibliographic record
Abstract
OBJECTIVES: The effectiveness of mechanical tooth cleaning, which plays a crucial role in biofilm control, depends on the type of brush and technique. Parents can refer to websites such as YouTube™ for guidance on the selection and use of electric toothbrushes. The objective of this study was to examine the usefulness, quality and accuracy of the information on YouTube™ videos about electric toothbrushes for children. MATERIALS AND METHODS: A search was performed on YouTube™ for English language videos using the terms 'electric toothbrush for kids' and 'kids' electric toothbrush'. From the first 100 results, 64 videos were selected for further analysis. The videos were analysed for views, likes/dislikes, number of comments, upload source, duration and time since video upload. The usefulness and the quality of the selected videos were also measured. RESULTS: The majority of the videos mentioned toothbrush design (71.9%, n = 46) and toothbrush heads (62.5%, n = 40). The videos were generally determined to be moderately useful (46.9%), whereas very useful videos were found less frequently (12.5%). Slightly useful videos were mostly uploaded by laypeople. Very useful videos had significantly higher video durations than moderately and slightly useful videos (p = 0.029 and p = 0.002, respectively). CONCLUSIONS: YouTube™ can be an important source of information for parents to learn about electric toothbrushes for their children. However, watching videos based on upload source and length of time may provide more accurate information on this topic. Also, dental healthcare professionals could be included more often to improve the usefulness and quality of the videos.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".