Depression, Loneliness and Cognitive Distortion among Young Unwed Pregnant Women in Malaysia: Counseling Implications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".