Digital Media Recruitment for Fall Prevention Among Older Chinese-American Individuals: Observational, Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Research in fall prevention programs has increased in recent years in response to the aging demographics of the United States. To date, limited research and outreach programs have focused on ethnic minorities due to increased cost, language barriers, and cultural differences. Digital media platforms may be a cost-effective avenue to initiate fall prevention programs for minority populations. OBJECTIVE: The objective of this study was to determine whether Facebook advertisements are a practical recruitment method for health education to the Chinese-speaking population. METHODS: This was an observational, cross-sectional study. We uploaded a video on fall education on YouTube and initiated an advertisement campaign on Facebook that was linked to the video. The target population was older adults aged >45 years who used Facebook and were presented with the advertisement (N=1039). We recorded metrics such as the number of unique individuals reached, the number of views of the advertisement, the number of clicks, user gender and age, and traffic sources to the advertisement. Data were analyzed with descriptive statistics. RESULTS: Our Facebook advertisement had 1087 views (1039 unique viewers). There were 121 link clicks with a click-through rate of 11.13% (121/1087). The cost per link click was approximately US $0.06. Among the viewers, 91.41% (936/1024) were females and 8.59% (88/1024) were males. In the 45-54 age group, the ad reached 50 people, with 1 link click (2.00%). In the 55-64 age group, the ad reached 572 people, with 57 link clicks (9.97%). In the ≥65 age group, the ad reached 417 people, with 63 link clicks (15.11%). CONCLUSIONS: Facebook was able to directly target the advertisement to the desired older ethnic population at a low cost. Engagement was highest among females and among those aged ≥65 years. Hence, our results suggest that Facebook can serve as an alternative platform for dissemination of health information to geriatric patients in addition to print-based and face-to-face communication.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".