Self-rated health and ethnicity: focus on indigenous populations
Bibliographic record
Abstract
OBJECTIVES: Self-rated health (SRH) is a commonly used measure in surveys to assess general health status or health-related quality of life. Differences have been detected in how different ethnic groups and nationalities interpret the SRH measure and assess their health. This review summarizes the research conducted on SRH within and between ethnic groups, with a focus on indigenous groups. STUDY DESIGN AND METHODS: A search of published academic literature on SRH and ethnicity, including a comprehensive review of all relevant indigenous research, was conducted using PubMed and summarized. RESULTS: A wide variety of research on SRH within ethnic groups has been undertaken. SRH typically serves as an outcome measure. Minority respondents generally rated their health worse than the dominant population. Numerous culturally-specific determinants of SRH have been identified. Cross-national and cross-ethnicity comparisons of the associations of SRH have been conducted to assess the validity of SRH. While SRH is a valid measure within a variety of ethnicities, differences in how SRH is assessed by ethnicities have been detected. Research in indigenous groups remains generally under-represented in the SRH literature. CONCLUSIONS: These results suggest that different ethnic groups and nationalities vary in SRH evaluations, interpretation of the SRH measure, and referents employed in rating health. To effectively assess and redress health disparities and establish culturally-relevant and effective health interventions, a greater understanding of SRH is required, particularly among indigenous groups, in which little research has been conducted.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".