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Record W2161387947 · doi:10.31826/9781463233983-024

Cultuial and Socio-Economic Factors in Health, Health Services and Prevention for Indigenous People

2010· book-chapter· en· W2161387947 on OpenAlexaboutno aff
Rakibul M. Islam, Mashhood Ahmed Sheikh

Bibliographic record

VenueGorgias Press eBooks · 2010
Typebook-chapter
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEnvironmental healthSocioeconomicsEconomic growthGeographyBusinessMedicineSociologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.292
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2010
Admission routes1
Has abstractyes

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