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Record W2905195803 · doi:10.1097/nmc.0000000000000488

Attitudes of Physicians, Midwives, and Nurses About Doulas

2018· article· en· W2905195803 on OpenAlexaboutno aff
Laura Lucas, Erin Wright

Bibliographic record

VenueMCN The American Journal of Maternal/Child Nursing · 2018
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLInclusion (mineral)ScopusMedicineMaternity careNursingFamily medicineMEDLINEPsychologyHealth carePsychological interventionSocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Evidence supports numerous positive clinical benefits of doula care. There are varying attitudes among physicians, midwives, and nurses toward support of doulas in a collaborative approach with women in labor. Tension and conflict with use of doulas may occur in some intrapartum settings in the United States. METHODS: A scoping review of the literature between January 2008 and January 2018 was conducted using PubMed, CINAHL, Google Scholar, and Scopus database to identify specific attitudes of physicians, midwives, and nurses toward doulas; 1,810 records were identified and initially reviewed. Inclusion criteria included original research published in the last 10 years and in the English language. Articles were excluded if the research was not original and if obstetrical providers' or nurses' attitudes toward doulas were not included. RESULTS: Three records met criteria for inclusion. All used a cross-sectional survey design. Two were set in Canada exclusively and one was inclusive of nurses and doulas in both Canada and the United States. Themes emerged that may explain the influence and variances in attitudes toward doulas and the support they provide to laboring women. CLINICAL IMPLICATIONS: More research is needed to identify attitudes of members of the maternity care team toward doulas and to better understand implications of their attitudes on working together collaboratively and on patient outcomes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.343

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 studies0.0000.001
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 head0.014
GPT teacher head0.345
Teacher spread0.331 · 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 designObservational
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

Citations25
Published2018
Admission routes1
Has abstractyes

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Same venueMCN The American Journal of Maternal/Child NursingSame topicMaternal and Perinatal Health InterventionsFrench-language works237,207