The Greenland population health survey 2018 – methods of a prospective study of risk factors for lifestyle related diseases and social determinants of health amongst Inuit
Bibliographic record
Abstract
Since 1993, regular population health surveys in Greenland have supported and monitored the public health strategy of Greenland and have monitored cardiometabolic and lung diseases. The most recent of these surveys included 2539 persons aged 15+ from 20 communities spread over the whole country. The survey instruments included personal interviews, self-administered questionnaires, blood sampling, anthropometric measurements, blood pressure, ECG, oral glucose test, pulmonary function, hand grip strength and chair stand test. Blood samples were analysed for glucose, glycated haemoglobin (HbA1c), insulin, incretin hormones, cholesterol, kidney function, fatty acids in erythrocyte membranes and mercury, urine for albumin-creatinine ratio, and aliquots were stored at -80°C for future use. Data were furthermore collected for studies of the gut microbiome and diabetes complications. Survey participants were followed up with register data. The potential of the study is to contribute to the continued monitoring of risk factors and health conditions as part of Greenland's public health strategy and to study the epidemiology of cardiometabolic diseases and other chronic diseases and behavioural risk factors. The next population health survey is planned for 2024. The emphasis of the article is on the methods of the study and results will be presented in other publications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".