The Socialization of Young Children’s Empathy for Pain: The Role of Mother– and Father–Child Reminiscing
Bibliographic record
Abstract
OBJECTIVE: Empathy for pain allows one to recognize, understand, and respond to another person's pain in a prosocial manner. Young children develop empathy for pain later than empathy for other negative emotions (e.g., sadness), which may be due to social learning. How parents reminisce with children about past painful events has been linked to children's pain cognitions (e.g., memory) and broader socioemotional development. The present study examined how parent-child reminiscing about pain may be linked to children's empathic behaviors toward another person's pain. METHODS: One hundred and fourteen 4-year-old children (55% girls) and for each, one parent (51% fathers) completed a structured narrative elicitation task wherein they reminisced about a past painful autobiographical event for the child. Children were then observed responding in a lab-based empathy task wherein they witnessed a confederate pretending to hurt themselves. Children's empathic behaviors and parent-child narratives about past painful events were coded using established coding schemes. RESULTS: Findings revealed that parents who used more neutral emotion language (e.g., How did you feel?) when discussing past painful events had children who exhibited more empathic concern in response to another's pain. Similarly, children who used more explanations when reminiscing about past painful events displayed more empathic concern about another's pain. CONCLUSIONS: Findings highlight a key role of parent-child reminiscing about the past pain in the behavioral expression of empathy for pain in young 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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".