Communicating health information with online videos
Bibliographic record
Abstract
Videos can create learning communities, increase communication richness, empower users and encourage identity formation. Online sites like YouTube share both professionally-produced videos and user-generated videos. Low-budget user-generated videos could offer new opportunities for promotion and awareness of health issues. Our study explores how a broad spectrum of people living in a small Canadian city engages with online videos for health information. A sample of adults who watch online videos participated in a survey with multi-media content. The study focus was to determine if they were seeking health information via online videos and to assess their responses to online videos on mental health issues. While 44% of participants never or rarely watched online videos containing health information, 90% believed that viewing short videos online produced by health professionals is a good way for people to access information about health. Participants then viewed, in random order, two short videos on mental health posted on YouTube– one user-generated, and the other professionally-developed by a mental health organization. After viewing the videos, participants reported high levels of interest and learning, being influenced by the video, and acceptance for the use of online video for increasing their awareness and knowledge of health information. Our results suggest that both short user-generated and professional online videos are potentially of interest to a wide range of people and are an influential medium of health information that can positively influence the viewers’ awareness, interest and learning on health issues.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".