Social Support and Technology Use and Their Association With Mental and Physical Health During the COVID-19 Pandemic Among Asian Americans: The COMPASS Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The global COVID-19 pandemic disproportionately affected Asian Americans and Pacific Islanders (AAPIs) and revealed significant health disparities with reports of increased discrimination and xenophobia. Among AAPIs, the pandemic exacerbated their social, linguistic, and geographic isolation. Social support may be especially important for AAPIs given the salience of collectivism as a cultural value. Another mechanism for support among AAPIs was technology use, as it is generally widespread among this population. However, older adults may not perceive the same benefits. OBJECTIVE: We examined social support and technology use and their relationships with mental and physical health outcomes through the COVID-19 pandemic among AAPIs. METHODS: Data were drawn from the COVID-19 Effects on the Mental and Physical Health of AAPI Survey Study (COMPASS) for the time period of October 2020 to February 2021. COMPASS was a cross-sectional, multilingual, national survey conducted online, by phone, and in person with AAPI adults who were ≥18 years of age, in collaboration with academic and community partners in the United States. Data were analyzed using multivariable linear regression using the outcome variables of mental and physical health with various predictors such as social support and technology use. We tested for interactions specific to age and ethnicity. RESULTS: Among 4631 AAPIs (mean age 45.9, SD 16.3 years; 2992/4631, 63.1% female), we found that (1) increased social support was associated with better physical health, (2) total social support was positively associated with better mental health, (3) higher technology use was associated with poorer mental health and inversely associated with poorer physical health, (4) the association of technology use with mental health was weaker among those with low social support (vs those with high social support), (5) adults younger than 60 years old (vs ≥60 years old) were more negatively affected with social support and mental health, and (6) Korean Americans appeared to be a high-risk group for poor physical health with increased technology use. CONCLUSIONS: Our paper identified mental and physical health needs along with supportive therapies observed among AAPIs during the pandemic. Future research on how social support can be leveraged, especially among AAPIs younger than 60 years old, and how various types of technology are being utilized are important to guide the recovery efforts to address both mental and physical disparities across communities as a result of the COVID-19 pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".