Infant Pain Regulation as an Early Indicator of Childhood Temperament
Bibliographic record
Abstract
BACKGROUND: There is considerable variability in infants' responses to painful stimuli, including facial and vocal expressions. This variability in pain-related distress response may be an indicator of temperament styles in childhood. OBJECTIVE: To examine the relationships among immunization pain outcomes (pain reactivity, pain regulation and parent ratings of infant pain) over the first year of life and parent report of early temperament. METHODS: A subset of parent-infant dyads in an ongoing Canadian longitudinal cohort was studied. Infant pain behaviours were coded using the Modified Behavior Pain Scale. Parental judgments of infant pain were recorded using the Numeric Rating Scale. Infant temperament was measured using the Infant Behaviour Questionnaire-Revised. Correlational analyses and multiple regressions were conducted. RESULTS: Multiple regressions revealed that the 12-month regulatory pain scores predicted parent ratings of the Negative Affectivity temperament dimension at 14 months of age. Parent ratings of infant pain at 12 months of age predicted parent ratings of the Orienting⁄Affiliation temperament dimension, with sex differences observed in this substrate. CONCLUSION: Pain-related distress regulation at one year of age appears to be a novel indicator of parent report of temperament ratings. Pain outcomes in the first six months of life were not related to parent temperament ratings.
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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 | 0.003 |
| 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 | 0.001 | 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".