The Roles of YouTube and WhatsApp in Dementia Education for the Older Chinese American Population: Longitudinal Analysis
Bibliographic record
Abstract
BACKGROUND: Dementia remains a stigmatized topic in the Chinese community. OBJECTIVE: This study aims to analyze and compare the usage of dementia educational YouTube videos and the modalities of video sharing over a 6-year period. METHODS: Dementia educational videos were uploaded to YouTube. Data was collected over a 6-year period. Results from the first 3 years were compared to those from the second 3 years using descriptive statistics and chi-square analysis. RESULTS: Over 6 years, the dementia educational videos generated a total watch time of 269,388 minutes, 37,690 views, and an average view duration of 7.1 minutes. Comparing the first and second 3-year periods of video performance data, there was a longer watch time (59,262 vs 210,126 minutes), more total views (9387 vs 28,303 views), and a longer average view duration (6.3 vs 7.4 minutes). Furthermore, WhatsApp has become a leading external traffic source and top sharing service, accounting for 43.5% (929/2137) and 67.0% (677/1011), respectively. CONCLUSIONS: Over 6 years, YouTube has become an increasingly popular tool to deliver culturally sensitive dementia education to Chinese Americans. WhatsApp continues to be the preferred method of sharing dementia education and has become a top external traffic source to dementia educational videos. Taken together, these social media platforms are promising means of reducing the disparity in dementia knowledge in linguistically and culturally isolated populations.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".