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Record W2781842261 · doi:10.5430/jnep.v8n6p48

On the relation of self-efficacy and coping with the experience of childbirth

2018· article· en· W2781842261 on OpenAlexvenueno aff
María J Sánchez-Cunqueiro, María Isabel Comeche, Domingo Docampo

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsChildbirthCoping (psychology)Self-efficacyPsychologyClinical psychologyLikert scaleMedicineObstetricsPregnancySocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Objective: To analyze the relationship between self-efficacy expectancies, the use of coping behavior strategies during labor and satisfaction after childbirth.Methods: A quantitative observational design was applied as part of a correlational study conducted in the maternity unit of a Hospital Complex that welcomes nearly 4,000 births each year at Vigo, Spain, between 2014 and 2015. A total of 276 low-risk pregnant women were recruited to undertake a self-assessment of their childbirth experience at two stages: within the last three months of pregnancy and within two weeks after labor. Data were collected through the Childbirth Self-Efficacy Inventory to measure self-efficacy expectancies as well as coping, along with a 6 items, 10-point Likert scale to measure satisfaction after childbirth.Results and conclusions: Pearson product-moment correlation supported the positive association of self-efficacy expectancies scores with coping during labor. Multivariate regression analysis also revealed gains in satisfaction after childbirth associated with coping during labor. Women with larger scores in self-efficacy were found to use coping strategies during labor, had a more positive evaluation of the childbirth experience and showed significant gains in satisfaction after childbirth. The study supports the efforts of healthcare professionals to increase satisfaction with the childbirth experience by helping to enhance self-efficacy and coping in pregnant women.

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 score0.801
Threshold uncertainty score0.093

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 studies0.0000.000
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 head0.073
GPT teacher head0.452
Teacher spread0.379 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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