Judgments of Infant Pain: The Impact of Caregiver Identity and Infant Age
Bibliographic record
Abstract
OBJECTIVE: To examine whether caregiver judgments of infant pain would vary systematically with different infant caregiver groups and infant age. METHODS: A total of 123 caregivers (41 parents, 41 in patient nurses, 41 pediatricians) viewed videotapes of the vigorous behavioral responses of healthy infants (aged 2, 4, 6, 12, and 18 months) to a routine immunization injection and provided ratings of both the affective distress and pain intensity observed. RESULTS: A principal components analysis of affective and intensity ratings yielded a weighted pain summary score for each injection event. Older infants were attributed significantly more pain than younger infants, even though the vigor of the behavioral reactions was experimentally controlled across age groups. A profile analysis contrasting observer groups indicated that pediatricians attributed significantly lower levels of pain than parents, while nurses were intermediate to the other groups, not significantly differing from either group. These systematic differences in judgments were consistent across infant age groups. CONCLUSIONS: The findings reveal systematic sources of significant variations in observer judgments of infant pain. Despite an absence of differences in the behavioral reactions of the children, both the type of caregiver and their knowledge of the child's age systematically influenced attributions of pain to infants. This work suggests the important role of caregiver role variation and perceived developmental maturity as determinants of infant pain judgments and highlights potential areas of difficulty in controlling the unnecessary suffering of infants.
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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 | 0.018 |
| 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".