Breast cancer screening literacy information on online platforms: A content analysis of YouTube videos
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The YouTube platform has great potential of serving as a healthcare resource due to its easy accessibility, navigability and wide audience reach. Breast cancer screening is an important preventative measure that can reduce breast cancer mortality by 40%. Therefore, platforms being used as a healthcare resources, such as YouTube, can and should be used to advocate for essential preventative measures such as breast cancer screening. METHODS: In this study, the usefulness of videos related to breast cancer and breast cancer screening were analyzed. Videos were first screened for inclusion and then were categorized into very useful, moderately useful, somewhat useful, and not useful categories according to a 10-point criteria scale developed by medical professionals based on existing breast cancer screening guidelines. Two reviewers independently assessed each video using the scale. RESULTS: 200 videos were identified in the preliminary analysis (100 for the search phrase 'breast cancer' and 100 for the search phrase 'breast cancer screening'). After exclusion of duplicates and non-relevant videos, 162 videos were included in the final analysis. We found the following distribution of videos: 4.3% very useful, 17.9% moderately useful, 39.5% somewhat useful, and 38.3% not useful videos. There was a significant association between each of the following and the video's level of usefulness: video length, the number of likes, and the uploading source. Longer videos were very useful, somewhat useful videos were the most liked, personally produced videos were the most not useful, and advertisements produced the highest ratio of very useful to not useful videos. CONCLUSION: It is necessary to create more reliable and useful healthcare resources for the general population as well as to monitor health information on easily accessible social platforms such as YouTube.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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 it