Cultuial and Socio-Economic Factors in Health, Health Services and Prevention for Indigenous People
Bibliographic record
Abstract
Summary: Indigenous people across the world experience more health related problems as compared to the population at large. So, this review article is broadly an attempt to highlight the important factors for indigenous peoples' health problems, and to recommend some suggestions to improve their health status. Standard database for instance, Pubmed, Medline, Google scholar, and Google book searches have been used to get the sources. Different key words, for example, indigenous people and health, socio-economic and cultural factors of indigenous health, history of indigenous peoples' health, Australian indigenous peoples' health, Latin American indigenous peoples' health, Canadian indigenous peoples' health, South Asian indigenous peoples' health, African indigenous peoples' health, and so on, have been used to find the articles and books. This review paper shows that along with commonplace factors, indigenous peoples' health is affected by some distinctive factors such as indigeneity, colonial and post-colonial experience, rurality, lack of governments' recognition etc., which non- indigenous people face to a much lesser degree. In addition, indigenous peoples around the world experience various health problems due to their varied socio-economic and cultural contexts. Finally, this paper recommends that the spiritual, physical, mental, emotional, cultural, economic, socio-cultural and environmental factors should be incorporated into the indigenous health agenda to improve their health status.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".