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Record W2791981284 · doi:10.1177/0844562118757096

Selection and Use of Health Services for Infants’ Needs by Indigenous Mothers in Canada: Integrative Literature Review

2018· review· en· W2791981284 on OpenAlexaffvenueabout
Amy Wright, Olive Wahoush, Marilyn Ballantyne, Chelsea Gabel, Susan M. Jack

Bibliographic record

VenueCanadian Journal of Nursing Research · 2018
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsIndigenousHealth careMedicinePopulationInfant mortalityHealth servicesNursingEnvironmental healthEconomic growth

Abstract

fetched live from OpenAlex

In Canada, Indigenous infants experience significant health disparities when compared to non-Indigenous infants, including significantly higher rates of birth complications and infant mortality rates. The use of primary health care is one way to improve health outcomes; however, Indigenous children may use health services less often than non-Indigenous children. To improve health outcomes within this growing population, it is essential to understand how caregivers, defined here as mothers, select and use health services in Canada. This integrative review is the first to critique and synthesize what is known of how Indigenous mothers in Canada experience selecting and using health services to meet the health needs of their infants. Themes identified suggest both Indigenous women and infants face significant challenges; colonialism has had, and continues to have, a detrimental impact on Indigenous mothering; and very little is known about how Indigenous mothers select and use health services to meet the health of their infants. This review revealed significant gaps in the literature and a need for future research. Suggestions are made for how health providers can better support Indigenous mothers and infants in their use of health services, based on what has been explored in the literature to date.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.752
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.441
Teacher spread0.357 · 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
GenreReview

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

Citations10
Published2018
Admission routes3
Has abstractyes

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