The effects of early relational antecedents and other factors on the parental sensitivity of mothers and fathers
Bibliographic record
Abstract
Abstract This study examines the effect of early relational antecedents (ERA, i.e. the quality of parenting parents recalled receiving as children), parenting stress, marital stress, socio‐economic factors and children's characteristics (gender and disability condition) on the parental sensitivity of mothers and fathers. The sample consisted of 116 mothers and 84 fathers of 117 eighteen month old children drawn from a larger longitudinal study on the adaptation of parents to a child with a disability. Thirty‐four children were diagnosed with Down syndrome (DS), 51 with a cleft lip and/or palate (CLP), and 32 were non‐disabled children. Multiple regression analyses reveal that mothers' sensitivity is best predicted by her level of education and family income, whereas fathers' sensitivity is best predicted by their ERA, marital stress, family income and the child's disability condition. Mothers with more education and a greater family income displayed a greater sensitivity to their children, as did fathers who perceive less marital stress, those with a greater family income and those who perceived their parents as less controlling. Also, fathers of children with DS displayed less sensitivity for their children than fathers of children with CLP or fathers of non‐disabled children. These results concord with many studies about the importance of socio‐economic factors, ERA, marital stress, parent's gender and children's factors in the understanding of parental sensitivity. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".