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Record W4392972973 · doi:10.26443/mjgh.v9i1.1322

Black Mothers in America

2020· article· en· W4392972973 on OpenAlexafffund
Olivia Frank, Alanna Miller, Jason Vu, Zoe Doran, J. Liu, Aleksandar Mihic

Bibliographic record

VenueMcGill Journal of Global Health · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsMcGill University
FundersMcGill University
KeywordsChildbirthPsychological interventionSocioeconomic statusRacismHealth equityInequalityHealth carePovertyInfant mortalityPolitical scienceDemographic economicsPsychologyMedicineEconomic growthEnvironmental healthNursingEconomicsPregnancyPopulation

Abstract

fetched live from OpenAlex

The United States has the highest rate of maternal mortality among high-income countries, despite spending the single-largest percentage of GDP on healthcare. This burden disproportionately afects Black mothers who experience a maternal mortality ratio that is four times that of White mothers. Tis case study demonstrates that the disparities in maternal outcomes between Black and White mothers are rooted in racial discrimination. This racial inequality manifests in part through increased allostatic load as a result of intergenerational experiences of racism; unequal access to high quality insurance coverage; and racial discrimination by healthcare practitioners. Potential interventions to explore include federal and state employment regulations that lessen the socioeconomic barriers preventing Black Americans from accessing quality insurance coverage; crosscultural training programs in healthcare facilities and teaching institutions; and a systematic shif toward holistic models of childbirth. Tough these interventions can serve to diminish the consequences felt on the individual level, collaborative multi-systemic change is necessary to address the social determinants of health that result in poor maternal outcomes on a national level.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.349
Teacher spread0.312 · 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 designNot applicable
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

Citations2
Published2020
Admission routes2
Has abstractyes

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