The Effect of Maternal Employment on the Elementary and Junior High School Students’ Mental Health in Maku
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Most experts view the childhood period as a foundation for shaping the individuals' fundamental future characteristics and behaviors. They believe that parents' personality and behavior quality exert a greater effect on the development of a child's personality than other factors. Given the mothers' role in children's mental health and considering the fact that children are a nation's future makers, the present study was designed to investigate the impact of maternal employment on students' mental health in Maku. MATERIALS & METHODS: The present study is descriptive and cross-sectional, and the population of the study encompasses all students in the fifth, sixth, and seventh grades (n=583) who are studying in 2013-2014 academic year in Maku. General Heath Questionnaire was employed for gathering data, and the SPSS software was used for analyzing the data. FINDINGS: The results of the study indicated that there was a significant difference between the mental health problems, somatic problems, social functioning, anxiety, and depression of the students with employed and non-employed mothers. In other words, the students with non-working mothers experienced greater mental disorders than those with working mothers. CONCLUSION: According to the findings of this study, it can be concluded that children with working mothers showed a better mental health than non-working mothers' children.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".