A Social Media–Based Intervention for Chinese American Caregivers of Persons With Dementia: Protocol Development
Bibliographic record
Abstract
BACKGROUND: Racial/ethnic minority and immigrant caregivers of persons with dementia experience high rates of psychosocial stress and adverse health outcomes. Few culturally tailored mobile health (mHealth) programs were designed for these vulnerable populations. OBJECTIVE: This study reports the development of a culturally tailored mHealth program called Wellness Enhancement for Caregivers (WECARE) to improve caregiving skills, reduce distress, and improve the psychosocial well-being of Chinese American family caregivers of persons with dementia. METHODS: Community-based user-centered design principles were applied in the program development. First, the structure and curriculum of the WECARE program were crafted based on existing evidence-based interventions for caregivers with input from 4 experts. Second, through working closely with 8 stakeholders, we culturally adapted evidence-based programs into multimedia program components. Lastly, 5 target users tested the initial WECARE program; their experience and feedback were used to further refine the program. RESULTS: The resulting WECARE is a 7-week mHealth program delivered via WeChat, a social media app highly popular in Chinese Americans. By subscribing to the official WECARE account, users can receive 6 interactive multimedia articles pushed to their WeChat accounts each week for 7 weeks. The 7 major themes include (1) facts of dementia and caregiving; (2) the enhancement of caregiving skills; (3) effective communication with health care providers, care partners, and family members; (4) problem-solving skills for caregiving stress management; (5) stress reduction and depression prevention; (6) the practice of self-care and health behaviors; and (7) social support and available resources. Users also have the option of joining group chats for peer support. The WECARE program also includes a back-end database that manages intervention delivery and tracks user engagement. CONCLUSIONS: The WECARE program represents one of the first culturally tailored social media-based interventions for Chinese American caregivers of persons with dementia. It demonstrates the use of community-based user-centered design principles in developing an mHealth intervention program in underserved communities. We call for more cultural adaptation and development of mHealth interventions for immigrant and racial/ethnic minority caregivers of persons with dementia.
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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 | 0.036 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".