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Record W2941217808 · doi:10.1097/jpn.0000000000000400

Obesity and Socioeconomic Disparities

2019· review· en· W2941217808 on OpenAlexaff
Cecilia M. Jevitt

Bibliographic record

VenueThe Journal of Perinatal & Neonatal Nursing · 2019
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineObesityPopulationEnvironmental healthGerontologyEndocrinology

Abstract

fetched live from OpenAlex

Obesity affects more than 35% of women aged 20 to 39 years in the United States. This article summarizes recent research that reconceptualizes obesity as adipose disease associated with smoking; socio-economic disparities in employment, education, healthcare access, food quality, and availability; and environmental toxins, ultimately altering microbiomes and epigenetics. Individual prenatal care of women with obesity includes early testing for diabetes, counseling on epigenetic diets, advice supporting weight gain within national guidelines, and vigilance for signs of hypertensive disorders of pregnancy. Intrapartum care includes mechanical cervical ripening measures, patience with prolonged labor, and uterotonic medication readiness in the event of postpartum hemorrhage. Postpartum care includes thrombus risk amelioration through early ambulation, use of compression stockings, and anticoagulation. Delays in lactogenesis II can be offset by measures to support early breastfeeding. Sociopolitical action by nurses at national, state, and community levels to reduce population disparities in racism, education, and employment; reduce pollution from obesogenic chemicals; and improve food quality and distribution policies is likely to have the broadest impact in future obesity reductions and prevention.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.038
GPT teacher head0.359
Teacher spread0.322 · 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 designOther design
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

Citations13
Published2019
Admission routes1
Has abstractyes

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