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Record W2068958309 · doi:10.5539/gjhs.v6n5p46

Exploring Women's Personal Experiences of Giving Birth in Gonabad City: A Qualitative Study

2014· article· en· W2068958309 on OpenAlexvenueno aff
Fariba Askari, Alireza Atarodi, Shirin Torabi, Mahdi Moshki

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchPsychological interventionMedicinePsychologyDevelopmental psychologyNursingObstetricsSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Women's health is an important task in society. The aim of this qualitative study that used a phenomenological approach was to explain women's personal experiences of giving birth in Gonabad city that had positive experiences of giving birth in order to establish quality cares and the related factors of midwifery cares for this physiological phenomenon. METHODS: The participants were 21 primiparae women who gave a normal and or uncomplicated giving birth in the hospital of Gonabad University of medical sciences. Based on a purposeful approach in-depth interviews were continued to reach data saturation. The data were collected through open and semi-structured interactional in-depth interviews with all the participants. All the interviews were taped, transcribed and then analyzed through a qualitative content analysis method to identify the concepts and themes. FINDINGS: Some categories were emerged. A quiet and safe environment was the most urgent need of the most women giving birth. Unnecessary routine interventions that are performed on all women regardless of their needs and should be avoided were considered such as: "absolute rest, establishing vein, frequent vaginal examinations, fasting and early Amniotomy". All the women wanted to take part actively in their giving birth, because they believed it could affect their giving birth. CONCLUSION: We hope that the women's experiences of giving birth will be a pleasant and enjoyable experience for all the mothers giving birth.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.162
GPT teacher head0.457
Teacher spread0.295 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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