Age‐related differences in the acute pain facial expression during infancy
Bibliographic record
Abstract
BACKGROUND: The ontogenetic perspective on the development of emotional expressions in infants holds that infants' facial and vocal expressions evolved to serve crucial communicative functions in infancy and contribute to infants' survival. Infants' facial expressions should be contextualized by their own developmental stage rather than presuppositions from verbal populations. The overall aim of this paper was to examine age differences in the temporal patterning of elucidated facial expressions in the first minute following vaccination injections. METHODS: One hundred infants were videotaped longitudinally (2, 4, 6 and 12 months) from 2007-2012 during their routine vaccination appointment over the first year of life and five major negative facial configurations were identified using BabyFACS. In the current study, facial configurations were graphed in 5-s epochs for 1-min post-vaccination and subsequently analysed for facial expression by time effects using Repeated Measures ANOVAs at each age. RESULTS: Clear differences in temporal patterns were displayed as infants aged. ANOVA analyses indicated significant facial expression by time interactions at each age. CONCLUSIONS: Facial expressions illustrating intense/moderate distress and sensory overload were prominent in the first 15 s at the 2-, 4- and 6-month vaccination. However, expressions showing regulation of distress occurred progressively earlier over 1 min post-needle in older infants, suggesting a significant shift in regulatory capacity of pain-related distress occurs after 6-months of age. SIGNIFICANCE: An important developmental milestone was identified in infants' ability to regulate distress at 6 months. Supporting parents' infant pain management is particularly critical in the first months of life as infants' initial facial expressions appear to be more reflective of an organism overwhelmed by distress.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".