A Diabetes Screening and Education Program for Chinese American Food Service Employees Delivered in Chinese
Bibliographic record
Abstract
Asian Americans have a higher prevalence of developing type 2 diabetes mellitus (T2DM) compared with White Americans. A two-phase evidence-based project developed specifically for Chinese American employees at an urban catering company worksite was led by a registered nurse/certified diabetes educator. The purpose of this project was to (a) identify Chinese employees at risk for T2DM, and (b) develop and implement a customized diabetes prevention program in Chinese. In Phase 1, Chinese employees were screened for T2DM risk factors using a Chinese version of the Canadian Diabetes Risk Assessment Questionnaire (CANRISK). Thirty-five people, who represented 58% of the Chinese employees, were screened; two were newly diagnosed with T2DM, and two were newly diagnosed with prediabetes based on the screening scores, nonfasting blood glucose, and hemoglobin (Hb) A1c tests. In Phase 2, 23 Chinese employees were interviewed and their remarks were used to modify the National Diabetes Prevention Program (DPP). Six Chinese employees participated and completed the DPP. Risk scores, nonfasting blood glucose, and HbA1c were obtained and pre- and postprogram data were compared. Upon completion of the program, participants showed an average reduction of nonfasting blood glucose of 30 mg/dL (1.7 mmol/L), and a reduction of HbA1c by 0.32 points (3 mmol/mol). This evidence-based project emphasizes the importance of screening for diabetes in the worksite setting and using linguistically sensitive materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".