Are there differences in health care utilization in areas with both Sami and non-Sami populations in Norway?\nThe SAMINOR 1 study
Bibliographic record
Abstract
Abstract\nBackground\nWestern countries (Australia, New Zealand, the United States and Canada) with an indigenous population can all report disparities in health status between the majority and the indigenous population. Corresponding differences have not been found among the indigenous population in Norway, the Sami. Nevertheless, concerns regarding under-utilization of health care services and health disparities have emerged from previous studies from the 1980s. \nObjective\nMore recent studies have not been able to confirm findings of under-utilization, and the previous assumptions are currently being challenged. To determine whether there are ethnic differences in health care utilization in areas with both Sami and non-Sami populations in Norway, individually derived and population-based data is needed.\nThus, this thesis seeks to investigate potential ethnic differences in the number of general practitioner (GP) visits during the past year. \nMaterial and Methods\nData used in this thesis stems from the SAMINOR 1 study; a cross-sectional study from 2003-2004 in northern Norway. Participants in this study include persons of Sami, Kven and/or Norwegian ethnicity in the same geographical area.\nConclusion\nThe findings in this thesis confirm findings from other recent studies; overall, small differences in the number of GP visits during the past year were found when comparing Sami and non-Sami women and men in rura
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".