Contextualization of Diabetes: A Review of Reviews from Organisation for Economic Co-operation and Development (OECD) Countries
Bibliographic record
Abstract
PURPOSE OF REVIEW: The prevalence of diabetes is rising around the world and represents an important public health concern. Unlike individual-level risk and protective factors related to the etiology of diabetes, contextual risk factors have been much less studied. Identification of contextual factors related to the risk of type 1 and type 2 diabetes in Organisation for Economic Co-operation and Development (OECD) countries may help health professionals, researchers, and policymakers to improve surveillance, develop policies and programs, and allocate funding. RECENT FINDINGS: Among 4,470 potential articles, 48 were included in this review. All reviews were published in English between 2005 and 2023 and were conducted in over 20 different countries. This review identified ten upstream contextual risk factors related to type 1 and type 2 diabetes risk, including income, employment, education, immigration, race/ethnicity, geography, rural/urban status, built environment, environmental pollution, and food security/environment. The ten upstream contextual risk factors identified this review may be integrated into diabetes research, surveillance and prevention activities to help promote better outcomes for people at risk or living with diabetes in OECD countries. Additional research is needed to better quantify the measures of associations between emerging key contextual factors and diabetes outcomes.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".