Supportive Care Needs in Chinese, Vietnamese, and Korean Americans With Metastatic Cancer: Mixed Methods Protocol for the DAWN Study
Bibliographic record
Abstract
BACKGROUND: Asian Americans with metastatic cancer are an understudied population. The Describing Asian American Well-Being and Needs in Cancer (DAWN) Study was designed to understand the supportive care needs of Chinese-, Vietnamese-, and Korean-descent (CVK) patients with metastatic cancer. OBJECTIVE: This study aims to present the DAWN Study protocol involving a primarily qualitative, convergent, mixed methods study from multiple perspectives (patients or survivors, caregivers, and health care professionals). METHODS: CVK Americans diagnosed with solid-tumor metastatic cancer and their caregivers were recruited nationwide through various means (registries, community outreach newsletters, newspapers, radio advertisements, etc). Potentially eligible individuals were screened and consented on the web or through a phone interview. The study survey and interview for patients or survivors and caregivers were provided in English, traditional/simplified Chinese and Cantonese/Mandarin, Vietnamese, and Korean, and examined factors related to facing metastatic cancer, including quality of life, cultural values, coping, and cancer-related symptoms. Community-based organizations assisted in recruiting participants, developing and translating study materials, and connecting the team to individuals for conducting interviews in Asian languages. Health care professionals who have experience working with CVK patients or survivors with metastatic solid cancer were recruited through referrals from the DAWN Study community advisory board and were interviewed to understand unmet supportive care needs. RESULTS: Recruitment began in November 2020; data collection was completed in October 2022. A total of 66 patients or survivors, 13 caregivers, and 15 health care professionals completed all portions of the study. We completed data management in December 2023 and will submit results for patients or survivors and caregivers to publication outlets in 2024. CONCLUSIONS: Future findings related to this protocol will describe and understand the supportive care needs of CVK patients or survivors with metastatic cancer and will help develop culturally appropriate psychosocial interventions that target known predictors of unmet supportive care needs in Chinese, Vietnamese, and Korean Americans with metastatic cancer. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50032.
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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 | 0.048 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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; 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".