Increasing health inequality among Inuit in Greenland from 1993 to 2018: Different patterns for household assets, urbanization and a sociocultural index as indicators of social position
Bibliographic record
Abstract
Income inequality affects population health and wellbeing negatively. In Greenland, health inequality has been shown to exist among social groups, regionally and according to urbanization, and between Inuit and migrants from Denmark. The purpose of the study was to compare the changes in health inequality from 1993 to 2018 according to three measures of social position, i.e. a socioeconomic measure (household assets), a measure of urbanization and a composite sociocultural index. We hypothesized that social inequality in health increased parallel to the increasing economic inequality in Greenland. The sample was based on four population health surveys conducted among the Inuit in Greenland in 1993, 2005-2010, 2014 and 2018. The total number of interviews was 9024 and the total number of individuals interviewed was 5829, as participants were invited to several surveys as part of a cohort. As statistical measure of social disparity we used the slope index of inequality (SII) adjusted for age and sex. Analyses were performed with daily smoking, suicidal thoughts and obesity as health outcomes. Daily smoking was most prevalent among participants with low social position whereas obesity was most prevalent among participants with high social position. With household assets as indicator of social position, the results showed high and increasing social inequality for both daily smoking and obesity. Social inequality for daily smoking increased over time also for urbanization and the sociocultural index. The hypothesis that social inequality increased over time was thus confirmed for daily smoking and obesity but not for suicidal thoughts. With the results from the present study there is solid evidence to guide prevention and health care towards social equality in health.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".