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Record W3201867547 · doi:10.1377/hlthaff.2021.00805

Reimagining Perinatal Mental Health: An Expansive Vision For Structural Change

2021· article· en· W3201867547 on OpenAlexaff
Vu-An Foster, Jessica M. Harrison, Caitlin R. Williams, Ifeyinwa V. Asiodu, Sequoia Ayala, Jasmine Getrouw-Moore, Nastassia K. Davis, Wendy Davis, Inas K. Mahdi, Aza Nedhari, Paulomi Niles, Sayida Peprah, Jamila Perritt, Monica R. McLemore, Fleda Mask Jackson

Bibliographic record

VenueHealth Affairs · 2021
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsHealth Care Foundation
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsMental healthPovertyOppressionPsychosocialRacismPrecarityAnxietyMental illnessHealth equityPsychologyMedicineNursingPsychiatrySociologyPolitical sciencePublic healthGender studies

Abstract

fetched live from OpenAlex

Diagnoses of depression, anxiety, or other mental illness capture just one aspect of the psychosocial elements of the perinatal period. Perinatal loss; trauma; unstable, unsafe, or inhumane work environments; structural racism and gendered oppression in health care and society; and the lack of a social safety net threaten the overall well-being of birthing people, their families, and communities. Developing relevant policies for perinatal mental health thus requires attending to the intersecting effects of racism, poverty, lack of child care, inadequate postpartum support, and other structural violence on health. To fully understand and address this issue, we use a human rights framework to articulate how and why policy makers must take progressive action toward this goal. This commentary, written by an interdisciplinary and intergenerational team, employs personal and professional expertise to disrupt underlying assumptions about psychosocial aspects of the perinatal experience and reimagines a new way forward to facilitate well-being in the perinatal period.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.711

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 studies0.0010.000
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 head0.050
GPT teacher head0.392
Teacher spread0.343 · 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
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

Citations33
Published2021
Admission routes1
Has abstractyes

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