The acceptance of social media video for knowledge acquisition, sharing and application: A com-parative study among YouTube users and TikTok Users’ for medical purposes
Bibliographic record
Abstract
YouTube and TikTok have gained increasing recognition as social network sites to support online knowledge acquisition, sharing, and application via social media platforms in the medical field. This study examines which aspect of TikTok and YouTube stimulates doctors, nurses, and any other YouTube and TikTok in the medical setting, to rely on them as sources of knowledge acquisition and sharing to keep their medical repertoire updated. A hybrid model is designed to investigate users’ acceptance of YouTube and TikTok as social media platforms. The model focuses on four main external factors: content richness, innovativeness, satisfaction, and enjoyment. These factors are connected with two TAM constructs which are perceived ease of use and perceived usefulness. The results have shown that both YouTube and TikTok are affected by PEOU, PU, personal innovativeness, flow theory, and content richness. Both social media networks provide up-to-date sources described as useful, enjoyable, and relevant. Nevertheless, the comparative results have shown that YouTube has deeply influenced users’ medical perception and knowledge compared to TikTok. It is created for the very mere purpose of socialization and self-expression. In contrast, YouTube is used for educational and non-educational purposes due to the type of uploaded content and time management. Therefore, TikTok developers and influencers should initiate highly specialized videos and create content that raises awareness of medical field 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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".