Poor self-reported health and its association with biomarkers among Canadian Inuit
Bibliographic record
Abstract
OBJECTIVES: To determine the extent to which demographic characteristics, clinical measurements and biomarkers were associated with poor self-reported health (SRH) among Inuit adults in the Canadian Arctic. STUDY DESIGN: Cross-sectional survey was adopted as the study design. METHODS: The International Polar Year Inuit Health Survey carried out in 36 Canadian Arctic communities in 2007 and 2008 included Inuit men and women, aged 18 years or older, recruited from randomly selected households. The main outcome measure was SRH, which was dichotomized into good health (excellent, very good and good responses) and poor health (fair and poor responses). RESULTS: Of the 2,796 eligible households, 1,901 (68%) households and 2,595 participants took part in the survey. The weighted prevalence of poor SRH was 27.8%. Increasing age was significantly associated with poor SRH. The relative risk ratios for poor SRH was 2.0 (95% confidence interval [CI] 1.3-3.1) for men aged 50 years or older and 2.3 (95% CI 1.7-3.0) for women aged 50 years or older, compared with men and women aged 29 years or younger. After adjusting for age, gender and body mass index, poor SRH was significantly associated with smoking status (odds ratio [OR]=1.5; CI 1.1-2.0), at-risk fasting glucose levels (≥ 6.1 mmol/L) (OR=2.5; 95%; CI 1.5-4.2) and elevated hs C-reactive protein levels (>3-≤ 10 mg/L) (OR=2.1; 95% CI 1.4-3.1). Poor SRH was also significantly associated with a hypertriglyceridemic waist phenotype (high-risk waist circumference ≥ 102 cm for men and ≥ 88 cm for women with high triglyceride levels, ≥ 1.7 mmol/L), adjusted for age and gender, OR=1.6; 95% CI 1.1-2.3. CONCLUSIONS: Clinically relevant indicators of chronic disease risk were related to subjective assessment of SRH among Inuit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".