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Record W2537897813 · doi:10.1080/0167482x.2016.1244522

A qualitative interview study into experiences of management of labor pain among women in midwife-led care in the Netherlands

2016· article· en· W2537897813 on OpenAlexaff
Trudy Klomp, Anke B. Witteveen, Ank de Jonge, Eileen K. Hutton, Antoine L. M. Lagro-Janssen

Bibliographic record

VenueJournal of Psychosomatic Obstetrics & Gynecology · 2016
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChildbirthNonprobability samplingLabor painQualitative researchNursingMedicinePerceptionPain managementPsychologyObstetricsPregnancyPhysical therapySociology

Abstract

fetched live from OpenAlex

INTRODUCTION: Many pregnant women are concerned about the pain they will experience in labor and how to deal with this. This study's objective was to explore women's postpartum perception and view of how they dealt with labor pain. METHODS: Semistructured postpartum interviews were analyzed using the constant comparison method. Using purposive sampling, we selected 17 women from five midwifery practices across the Netherlands, from August 2009 to September 2010. RESULTS: Women reported that control over decision making during labor (about dealing with pain) helped them to deal with labor pain, as did continuous midwife support at home and in hospital, and effective childbirth preparation. Some of these women implicitly or explicitly indicated that midwives should know which method of pain management they need during labor and arrange this in good time. DISCUSSION: It may be difficult for midwives to discriminate between women who need continuous support through labor without pain medication and those who genuinely desire pain medication at a certain point in labor, and who will be dissatisfied postpartum if this need is unrecognized and unfulfilled.

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.034
GPT teacher head0.398
Teacher spread0.364 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

Explore more

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