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Record W2889177230 · doi:10.1177/1049732318794205

<i>Peyakohewamak</i> —Needs of Involved Nehiyaw (Cree) Fathers Supporting Their Partners During Pregnancy: Findings From the ENRICH Study

2018· article· en· W2889177230 on OpenAlexafffund
Richard T. Oster, Grant Bruno, Maria Mayan, Ellen L. Toth, Rhonda C. Bell

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates - Health Solutions
KeywordsPhotovoiceFlexibility (engineering)Inclusion (mineral)IndigenousQualitative researchPregnancyPsychologyParticipatory action researchFaithCommunity-based participatory researchDevelopmental psychologyNursingSocial psychologyMedicineSociology

Abstract

fetched live from OpenAlex

We sought to understand the needs of involved Nehiyaw (Cree) fathers who supported their partners during pregnancy. We used qualitative description and a community-based participatory research approach. We carried out in-depth semi-structured interviews with six Nehiyaw fathers. Four also participated in photovoice and follow-up interviews. All data were content analyzed qualitatively. Fathers felt they had to support their partners and overcome challenges resulting from intergenerational colonial impacts (residential schools particularly) by reclaiming their roles and acknowledging the pregnancy as a positive change. Providing support was possible through their own strong support system stemming from family, faith, culture, and a stable upbringing with positive male role models and intact Nehiyaw kinships. Perinatal programming did little to include fathers. Attempts to improve perinatal care and outcomes should allow more inclusion of and support for Indigenous fathers through genuinely incorporating into care traditional culture and Elders, families, flexibility, cultural understanding, and reconciliation.

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)0.024
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0150.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.292
GPT teacher head0.553
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.

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

Citations13
Published2018
Admission routes2
Has abstractyes

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