Stress and emotion recognition predict the relationship between a history of maltreatment and sensitive parenting behaviors: A moderated-moderation
Bibliographic record
Abstract
Our study proposes to examine how stress and emotion recognition interact with a history of maltreatment to influence sensitive parenting behaviors. A sample of 58 mothers and their children aged between 2 and 5 years old were recruited. Parents' history of maltreatment was measured using the Child Trauma Questionnaire. An emotion recognition task was performed. Mothers identified the dominant emotion in morphed facial emotion expressions in children. Mothers and children interacted for 15 minutes. Salivary cortisol levels of mothers were collected before and after the interaction. Maternal sensitive behaviors were coded during the interaction using the Coding Interactive Behavior scheme. Results indicate that the severity of childhood maltreatment is related to less sensitive behaviors for mothers with average to good abilities in emotion recognition and lower to average increases in cortisol levels following an interaction with their children. For mothers with higher cortisol levels, there is no association between a history of maltreatment and sensitive behaviors, indicating that higher stress reactivity could act as a protective factor. Our study highlights the complex interaction between individual characteristics and environmental factors when it comes to parenting. These results argue for targeted interventions that address personal trauma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".