Assessing the Risks and Cultural Relativity of Diabetes in Black Individuals of African Caribbean Ancestry (ACB) aged 18-39 Years in Toronto
Bibliographic record
Abstract
Context Diabetes rates are high in Black and some other ethnic communities, often leading to more severe complications. We conducted a study to identify the prevalence and risk of diabetes among African Caribbean Black (ACB) individuals aged 18-39 and to assess the sensitivity of glycated hemoglobin (HbA1c) compared to oral glucose tolerance test (OGTT) to diagnose diabetes. Methods In this mixed-methods study, Maximum Variation sampling was used to recruit 272 ACB participants from 14 African and five Caribbean countries from Toronto. Participants’ height, weight, waist circumference, HbA1c, OGTT, demographic, and behavioural data were collected. SPSS was used to analyze the quantitative data. Results The study found that 1.5% of participants had diabetes, 9.2% had prediabetes, and 44.9% were at risk of developing diabetes. The mean value of HbA1c, FBS, and 2hPG was 5.5%, 4.8 mmol/L, and 5.7 mmol/L respectively. The mean BMI was 28.2 kg/m2, and waist circumference was 85.8 cm. The study found a correlation between glucose intolerance and increasing Body Mass Index (BMI) and Waist Circumference (WC). Dietary habits, physical inactivity, and mental health challenges were risk factors among the participants. HbA1c was found to be a more sensitive and culturally acceptable screening measure than OGTT in diagnosing diabetes. Conclusions ACB individuals are at high risk of having diabetes, requiring culturally tailored peer-based health promotion strategies to reduce diabetes prevalence and risk. HbA1c is a culturally acceptable and statistically more capable measure than OGTT in identifying individuals with prediabetes. Further longitudinal research is needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".