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Record W7098215200

COMMENTARY A Review of Aboriginal Women’s Physical and Mental Health Status in Ontario

2016· article· en· W7098215200 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSteroid Chemistry and Biochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsCensusMental healthCommissionPopulationPublic healthSubstance use
DOInot available

Abstract

fetched live from OpenAlex

In traditional Aboriginal * cultures, women are the givers of life, and this role is highlyrespected. Unfortunately, today many Aboriginal women face greater health risks thanwomen in the general population.1 The following presents a review of Aboriginal women’s health status in Ontario, with particular focus on causes of mortality and morbidity; namely the incidence and prevalence of heart disease, diabetes, suicide, cancer, depression, substance use, and family violence in comparison to women of Ontario more generally. The data presented here have been compiled from health status data regarding urban and community Aboriginal women in Ontario; and when these are not available, national data are presented. The literature on American Indians in the United States is not reviewed. The Ontario First Nations Regional Health Survey2 (OFNRHS) presents repre-sentative data from Ontario First Nation (FN) women and children living in Aboriginal communities across the province. These rates were compared with those of Ontario respondents in general from the National Population Health Survey3 (NPHS). Statistics Canada has limited census data on Aboriginal women living in urban environments. The Aboriginal Peoples Survey (APS4) and the Royal Commission on Aboriginal Peoples Report (RCAP5) also present some national data, but without comparison groups. The Ontario Federation of Indian Friendship Centres6 has recently published data on the

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.005
GPT teacher head0.268
Teacher spread0.263 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2016
Admission routes1
Has abstractyes

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