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Record W2796986042 · doi:10.1177/1049732318765720

Rural Postpartum Women With Substance Use Disorders

2018· article· en· W2796986042 on OpenAlexaff
Debra Kramlich, Rebecca Kronk, Lenora Marcellus, Alison M. Colbert, Karen Jakub

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEmpathyAddictionService providerOpioid use disorderNursingSubstance usePsychologyRural areaMedicinePsychiatryService (business)BusinessOpioid

Abstract

fetched live from OpenAlex

The incidence of perinatal opioid use and neonatal withdrawal continues to rise rapidly in the face of the growing opioid addiction epidemic in the United States, with rural areas more severely affected. Despite decades of research and development of practice guidelines, maternal and neonatal outcomes have not improved substantially. This focused ethnography sought to understand the experience of accessing care necessary for substance use disorder recovery, pregnancy, and parenting. Personal accounts of 13 rural women, supplemented by participant observation and media artifacts, uncovered three domains with underlying themes: challenges of getting treatment and care (service availability, distance/geographic location, transportation, provider collaboration/coordination, physical and emotional safety), opportunities to bond (proximity, information), and importance of relationships (respect, empathy, familiarity, inclusion, interactions with care providers). Findings highlight the need for providers and policy makers to reduce barriers to treatment and care related to logistics, stigma, judgment, and lack of understanding of perinatal addiction.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.180
GPT teacher head0.500
Teacher spread0.320 · 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 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

Citations62
Published2018
Admission routes1
Has abstractyes

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