Forecasting of girls’ depression symptoms from mothers’ attachment style
Bibliographic record
Abstract
Introduction: Mother as the main attachment figure has undeniable effect on affective development of children. The aim of the current study was to investigate the relationship and predictability of girls’ depression symptoms through mothers’ attachment style. Method: The present study is of descriptive correlational design. The sample included 388 individuals of the second and the fourth grade students of elementary school that had been chosen through multistage random sampling. The Ontario Mental Health and Adults Attachment Style (AAS) questionnaires were used. Data were analyzed using Pearson correlation and multiple regression analysis. Results: The results showed a significant relationship between ambivalent (r=0.41), avoidant (r=0.32) and secure (r=-0.18) mothers’ attachment style and the girls’ depression symptoms. Final results showed that mothers’ ambivalent attachment style had the most power to predict girls’ depression symptoms. Therefore, mothers’ ambivalent attachment style can predict 0.12% of attachment problems in children. Furthermore, avoidant and secure mother’s attachment styles had 0.10% and 0.4% power to predict girls’ depression symptoms. Conclusion: Supporting the attachment theory, the results of this study show the importance of mother-child interaction. Mothers’ insecure attachment style can be a strong predictor of girls’ depression symptoms. Attachment based therapies may be useful to help treat depression in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".