Parenting stress, general distress, and coparenting quality: An <scp>Actor–Partner</scp> Interdependence Moderation Model
Bibliographic record
Abstract
Abstract Mothers and fathers who experience parenting stress are more likely to show symptoms of distress, such as elevated levels of anxiety and/or depression. Identifying the buffering or exacerbating factors that might moderate the association between parenting stress and general distress can help inform theoretical models aimed at better understanding the reality faced by parents during challenging times, as well as improve intervention strategies. The objective of this study was to examine whether coparenting quality plays a moderating role in the association between parenting stress and general distress in parents of children in middle childhood. Eighty‐one couples were asked to complete questionnaires pertaining to their levels of parenting stress, the quality of their coparenting relationship, and their symptoms of distress. Using an Actor–Partner Interdependence Moderation Model, our results revealed that elevated levels of parenting stress were associated with elevated levels of general distress, whereas higher scores of coparenting quality were associated with fewer symptoms of distress. Our results also showed a moderating role for coparenting quality in the association between actor parenting stress and actor/partner distress. These findings highlight the importance of pursuing the investigation of dyadic effects within personal relationships.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".