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Record W4281730828 · doi:10.1097/aco.0000000000001131

Limited English proficiency in the labor and delivery unit

2022· review· en· W4281730828 on OpenAlexaff
Brandon M. Togioka, Katherine M. Seligman, Carlos Delgado

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2022
Typereview
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsUnit (ring theory)BusinessPsychologyMathematics education

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Limited English proficiency (LEP) impacts patient access to safe and comprehensive care during the antepartum, intrapartum, and postpartum periods. In this review, we explore disparities in care delivery and outcomes that LEP women experience, and discuss the importance of providing language concordant care and using interpretation services appropriately. RECENT FINDINGS: The number of individuals with LEP is steadily increasing in the United States. Pregnant women with LEP suffer disparities in obstetric care and are at risk for postpartum depression, breastfeeding difficulties, and substandard newborn care after neonatal ICU discharge because of insufficient education. Addressing these issues requires the implementation of language concordant care and education, along with the utilization of medically trained interpreters. Although further evidence is needed, the authors support these interventions to improve patient satisfaction, decrease medical errors, and curtail misdiagnoses. SUMMARY: The pregnant woman with limited English proficiency is at risk of receiving suboptimal care and experiencing negative outcomes during the antepartum, intrapartum, and postpartum periods. The use of medically trained interpreters and the provision of language concordant care, through workforce diversification and the creation of forms and educational materials in diverse languages, can improve patient safety, outcomes, and quality of care.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
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 head0.319
GPT teacher head0.510
Teacher spread0.191 · 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.

Study designNot applicable
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

Citations15
Published2022
Admission routes1
Has abstractyes

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