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Record W3005218878 · doi:10.1111/nin.12338

Caring for Indigenous families in the neonatal intensive care unit

2020· article· en· W3005218878 on OpenAlexafffundabout
Amy Wright, Marilyn Ballantyne, Olive Wahoush

Bibliographic record

VenueNursing Inquiry · 2020
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalMcMaster UniversityUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsIndigenousNeonatal intensive care unitIntensive care unitNursingUnit (ring theory)MedicinePsychologyIntensive care medicinePediatrics

Abstract

fetched live from OpenAlex

Inequitable access to health care, social inequities, and racist and discriminatory care has resulted in the trend toward poorer health outcomes for Indigenous infants and their families when compared to non-Indigenous families in Canada. How Indigenous mothers experience care during an admission of their infant to the Neonatal Intensive Care Unit has implications for future health-seeking behaviors which may influence infant health outcomes. Nurses are well positioned to promote positive health care interactions and improve health outcomes by effectively meeting the needs of Indigenous families. This qualitative study was guided by interpretive description and the Two-Eyed Seeing framework and aimed to understand how Indigenous mothers experience accessing and using the health care system for their infants. Data were collected by way of interviews and a discussion group with self-identifying Indigenous mothers of infants less than two years of age living in Hamilton, Ontario, Canada. Data underwent thematic analysis, identifying nursing strategies to support positive health care interactions and promote the health and wellness of Indigenous infants and their families. Building relationships, providing holistic care, and taking a trauma-informed approach to the involvement of child protection services are three key strategies that nurses can use to positively impact health care experiences for Indigenous families.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.343

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 head0.065
GPT teacher head0.325
Teacher spread0.260 · 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 designQualitative
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

Citations21
Published2020
Admission routes3
Has abstractyes

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