Family-Based, Culturally Responsive Intervention for Chinese Americans With Diabetes: Lessons Learned From a Literature Review to Inform Study Design and Implementation
Bibliographic record
Abstract
Background The prevalence of diabetes in the United States is very high, and Chinese peoples with diabetes are estimated to comprise 50% of the total cases. Rates of diabetes continue to rise among Chinese and Chinese American people; however, research regarding effective diabetes interventions for this minority group is sparse. Objective A literature review was conducted to determine a study design and interventions for future studies investigating the efficacy of a family-based intervention to improve diabetes care for Chinese Americans. Methods The review was conducted from January 2023 to April 2023. The PubMed, CINAHL, ScienceDirect, ProQuest, Google Scholar, Scopus, and Cochrane Central Register of Controlled Trials databases were searched. The key search terms were “diabetes type 2,” “Chinese patients,” “minority patients,” “interventions for diabetes,” “diabetes and family,” “culturally responsive interventions for diabetic patients,” “family education for diabetes,” and “diabetes in China.” Results The initial search retrieved 2335 articles, and 10 articles met the selection criteria to examine the efficacy of family-based interventions for Chinese American people. The review showed that providing multiple sessions of education and counseling for both patients and family members is promising for improving diabetes care. Recruitment of 20 to 60 dyads consisting of a patient and a family member can help assess family dynamics in the process of diabetes care, such as food shopping and preparation, and of diabetes management to further evaluate the efficacy of an intervention. Glycated hemoglobin (HbA1c) was the most often used primary outcome. Other secondary outcomes included knowledge and efficacy in diabetes management and self-care activities related to diabetes care. Conclusions A family-based intervention is essential for optimizing diabetes care for Chinese Americans. Thus, recruitment of a dyad consisting of a patient and a family member is important to investigate the efficacy of a family-based intervention for improving diabetes care in this population. Strategies for improving recruitment and retention of dyads were identified. In addition, technology can be used to promote the delivery of interventions to patients, which in turn increases efficacy. This review can help researchers investigate the efficacy of family-based interventions for promoting diabetes management by designing culturally appropriate study protocols and interventions.
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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 | 0.102 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".