Recognition of children’s emotional facial expressions among mothers reporting a history of childhood maltreatment
Bibliographic record
Abstract
Several studies have shown that child maltreatment is associated with both positive and negative effects on the recognition of facial emotions. Research has provided little evidence of a relation between maltreatment during childhood and young adults' ability to recognize facial displays of emotion in children, an essential skill for a sensitive parental response. In this study, we examined the consequences of different forms of maltreatment experienced in childhood on emotion recognition during parenthood. Participants included sixty-three mothers of children aged 2 to 5 years. Retrospective self-reports of childhood maltreatment were assessed using the short form of the Childhood Trauma Questionnaire (CTQ). Emotion recognition was measured using a morphed facial emotion identification task of all six basic emotions (anger, disgust, fear, happiness, sadness, and surprise). A Path Analysis via Structural Equation Model revealed that a history of physical abuse is related to a decreased ability to recognize both fear and sadness in children, whereas emotional abuse and sexual abuse are related to a decreased ability to recognize anger in children. In addition, emotional neglect is associated with an increased ability to recognize anger, whereas physical neglect is associated with less accuracy in recognizing happiness in children's facial emotional expressions. These findings have important clinical implications and expand current understanding of the consequences of childhood maltreatment on parents' ability to detect children's needs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".