The Impact of Maternal Behavior on Children's Pain Experiences: An Experimental Analysis
Bibliographic record
Abstract
OBJECTIVE: To provide an experimental investigation of the impact of maternal behavior on children's pain experiences. METHOD: Participants were 120 healthy children (60 boys, 60 girls) between the ages of 8 and 12 years and their mothers. Mothers were randomly assigned and trained to interact with their children in one of three ways while the children were exposed to lab-induced cold pressor pain: (1) a pain-promoting interaction, (2) a pain-reducing interaction, and (3) a no training control group. Training was based on behaviors presumed to have the expected impact, as based on correlational studies reported in the literature. Children's pain experiences during the cold pressor were assessed using self-reports of intensity and affect, coding of facial activity, tolerance, and heart rate responsiveness. RESULTS: Girls whose mothers interacted with them in the pain-promoting manner reported more pain than daughters of mothers in the control group, who in turn reported more pain than girls whose mothers interacted with them in the pain-reducing manner. This effect was not significant for boys. Maternal interaction type had no effect on children's pain affect, facial activity, tolerance, or heart rate. CONCLUSIONS: Results indicate that maternal behavior can have a direct impact on their daughters' subjective reports of pain. These data support the importance of social learning factors in influencing children's pain experiences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".