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Record W2154403255 · doi:10.5539/gjhs.v4n3p139

Self-Care Behaviors among Thai Primigravida Teenagers

2012· article· en· W2154403255 on OpenAlexvenueno aff
Suphawadee Panthumas, Wirin Kittipichai, Supachai Pitikultang, Kanittha Chamroonsawasdi

Bibliographic record

VenueGlobal Journal of Health Science · 2012
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
FundersFaculty of Public Health, Mahidol UniversityMahidol UniversityChina Medical Board
KeywordsMedicineSelf carePregnancyDescriptive statisticsFamily medicineSelf-efficacyHealth careClinical psychologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate predictive factors of the self-care behaviors among Thai teenagers with primigravida. The samples of 206 primigravida teenagers attending ANC clinics of six hospitals in the North-Eastern region of Thailand were included. Data collection was done through self administered-questionnaire. Scales of the questionnaire had reliability coefficients ranging from 0.72 - 0.92. The data were analyzed by using descriptive and inferential statistics. The results revealed that the percentage-mean score of overall self-care behavior was 76.91. The percentage-mean scores of self-care behaviors in specific trimester were found that the score in the second trimester was lower than the scores in the first and third trimesters (57.58, 60.45, and 64.65, respectively). Factors associated with overall self-care behavior were perceived self-efficacy, perceived social support from family, knowledge on self-care during pregnancy, accessibility to health services, self-esteem and age (r = 0.47, 0.34, 0.28, 0.24, 0.19, and 0.15, respectively). Perceived self-efficacy and knowledge on self-care during pregnancy were the two considerable predictors accounted for 25% of the variance in the self-care behaviors of Thai teenagers with primigravida.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.347
Teacher spread0.329 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

Explore more

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