Mothers' facial expressions of pain and fear and infants' pain response during immunization
Bibliographic record
Abstract
The goal of the current study was to examine the relationship between mothers' spontaneous facial expressions of pain and fear immediately preceding their infants' immunizations and infants' facial expressions of pain immediately following immunizations. Infants' observations of mothers' faces prior to immunization also were examined to explore whether these observations moderated the effect of mothers' facial expressions on infant pain. The final sample included 58 mothers and their infants. Video data were used to code maternal facial expressions, infants' observations, and infants' expressions of pain. Infants who observed their mothers' faces had mothers who expressed significantly more fear pre-needle. Furthermore, mothers' facial expressions of mild fear pre-needle were associated with lower levels of infants' pain expression post-needle. A regression analysis confirmed maternal facial expressions of mild fear pre-needle as the strongest predictor of infant pain post-needle after controlling for infants' observations of mothers' faces. Mothers' subtle facial expressions of fear may indicate a relationship history of empathic caregiving that functions to support infants' abilities to regulate distress following painful procedures. Interventions aimed at improving caregiver sensitivity to infants' emotional cues may prove beneficial to infants in pain. Future directions in research are discussed.
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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 | 0.007 |
| 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 | 0.000 | 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".