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Record W2157496032 · doi:10.1177/1049732314545089

Diabetes in Pregnancy Among First Nations Women

2014· article· en· W2157496032 on OpenAlexafffundabout
Richard T. Oster, Maria Mayan, Ellen L. Toth

Bibliographic record

VenueQualitative Health Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPregnancyAutonomyMedicineQualitative researchDiabetes mellitusEthnographyHealth careNursingFamily medicineGerontologyObstetricsSociologyPolitical science

Abstract

fetched live from OpenAlex

We conducted a focused ethnography with 12 First Nations women who had had diabetes in pregnancy to understand their real-life experiences and find ways to improve care for those with diabetes in pregnancy. We carried out unstructured interviews that were recorded, transcribed, and subject to qualitative content analysis. The experience of diabetes in pregnancy is one wrought with difficulties but balanced to some degree by positive lifestyle changes. Having a strong support system (family, health care, cultural/community, and internal support) and the necessary resources (primarily awareness/education) allows women to take some control of their health. Efforts to improve pregnancy care for First Nations women should take a more patient-centered care approach and strive to enhance the support systems of these women, increase their sense of autonomy, and raise awareness of diabetes in pregnancy and its accompanying challenges.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.178
GPT teacher head0.533
Teacher spread0.355 · 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 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

Citations27
Published2014
Admission routes3
Has abstractyes

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