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Record W2268724736 · doi:10.1353/hpu.2016.0030

Racism and Oral Health Outcomes among Pregnant Canadian Aboriginal Women

2016· article· en· W2268724736 on OpenAlexfundaboutno aff
Herenia P. Lawrence, Jaime Cidro, Sonia Isaac-Mann, Sabrina Peressini, Marion Maar, Robert J. Schroth, Janet N Gordon, Laurie Hoffman‐Goetz, J. Richard Broughton, Lisa Jamieson

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchHealth CanadaShandong Academy of Sciences
KeywordsRacismMedicineHealth equityDemographyGender studiesSociologyNursingPublic health

Abstract

fetched live from OpenAlex

This study assessed links between racism and oral health outcomes among pregnant Canadian Aboriginal women. Baseline data were analyzed for 541 First Nations (94.6%) and Métis (5.4%) women in an early childhood caries preventive trial conducted in urban and on-reserve communities in Ontario and Manitoba. One-third of participants experienced racism in the past year determined by the Measure of Indigenous Racism Experience. In logistic regressions, outcomes significantly associated with incidents of racism included: wearing dentures, off-reserve dental care, asked to pay for dental services, perceived need for preventive care, flossing more than once daily, having fewer than 21 natural teeth, fear of going to dentist, never received orthodontic treatment and perceived impact of oral conditions on quality of life. In the context of dental care, racism experienced by Aboriginal women can be a barrier to accessing services. Programs and policies should address racism's insidious effects on both mothers' and children's oral health outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.344
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations50
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Health Care for the Poor and UnderservedSame topicIndigenous Health, Education, and RightsFrench-language works237,207