Methodology Considerations in Studying Mental Health, Sleep Quality, and Biopsychosocial Determinants Among Chinese and Korean Americans During the COVID-19 Pandemic
Bibliographic record
Abstract
Asians are one of the fastest-growing racial groups in the United States. The mental health of Asian Americans, particularly regarding depression and anxiety, needs significant attention. Various biopsychosocial factors interact to influence the risks of depression, anxiety, and sleep quality among Asian Americans. Currently, multiple methodological issues exist in the research of Asian Americans, such as limited data collection using Asian languages and inconsistent reporting of race and ethnicity data, which may be lacking entirely. All these methodological issues in research may account for the seemingly low prevalence rates of mental health problems among Asian Americans. In our study on mental health and sleep quality among Chinese and Korean Americans, we adopted multiple data collection strategies during the COVID-19 pandemic, including using culturally adaptive and validated measures as well as operating culture-sensitive procedures in the recruitment and data collection. The successful use of these strategies could promote early detection and personalized treatment of depression, anxiety, and sleep disturbance among Asian Americans. These strategies would further improve health care service use in this population. International Registered Report Identifier IRRID: RR2-10.1136/bmjopen-2020-047281.
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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 | 0.321 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".