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Record W2464185865 · doi:10.5539/ass.v12n8p104

Depression, Loneliness and Cognitive Distortion among Young Unwed Pregnant Women in Malaysia: Counseling Implications

2016· article· en· W2464185865 on OpenAlexvenueno aff
Rohany Nasir, Zainah Ahmad Zamani, Rozainee Khairudin, Wan Shahrazad Wan Sulaiman, Mohd Norahim Mohd Sani, Aizan Sofia Amin

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessPsychologyDepression (economics)CognitionWelfareScale (ratio)Developmental psychologyClinical psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

<p>Inability to meet the demands and challenges resulting from the rapid social and economic growth bring about social and psychological problems among youths and their families. One of the problems that young women are facing now is unwed pregnancies. Unwed pregnancies bring about negative social and psychological effects. The objective of this quantitative study is to ascertain the relationships among depression, loneliness and cognitive distortion. Respondents for this study were 150 young unwed pregnant women whose age ranged between 14 and 29 years old who were placed in shelters for unwed pregnant women run by the Social Welfare Department and various non governmental agencies throughout Malaysia. Four research instruments were used namely: Information on the respondents’ background, UCLA Loneliness Scale, Reynolds Adolescents Depression Scale (RADS) and Cognitive Distortion Scale (CDS). Results of the study showed that there were positive significant correlations between depression and loneliness, depression and cognitive distortion and loneliness and cognitive distortion. This paper also discussed the implications of the research findings on counselling and psychotherapy for the unwed pregnant women. Counselling and psychotherapy should focus on giving strength and hope for the young women to rebuild their life.</p>

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
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 head0.015
GPT teacher head0.323
Teacher spread0.308 · 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.

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

Citations15
Published2016
Admission routes1
Has abstractyes

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