Naturalistic parental pain management during immunizations during the first year of life: Observational norms from the OUCH cohort
Bibliographic record
Abstract
No research to date has descriptively catalogued what parents of healthy infants are naturalistically doing to manage their infant's pain over immunization appointments during the first year of life. This knowledge, in conjunction with an understanding of the relationships different parental techniques have with infant pain-related distress, would be useful when attempting to target parental pain management strategies in the infant immunization context. This study presents descriptive information about the pain management techniques parents have chosen and examines the relationships these naturalistic techniques have with infant pain-related distress during the first year of life. A total of 760 parent-infant dyads were recruited from 3 pediatric clinics in Toronto, ON, Canada, and were naturalistically followed and videotaped longitudinally over 4 immunization appointments during the infant's first year of life. Infants were full-term, healthy babies. Videotapes were subsequently coded for infant pain-related distress behaviors and parental pain management techniques. After controlling for preceding infant pain-related distress levels, parent pain management techniques accounted for, at most, 13% of the variance in infant pain-related distress scores. Across all age groups, physical comfort, rocking, and verbal reassurance were the most commonly used nonpharmacological pain management techniques. Pacifying and distraction appeared to be most promising in reducing needle-related distress in our sample of healthy infants. Parents in this sample seldom used pharmacological pain management techniques. Given the psychological and physical repercussions involved with unmanaged repetitive acute pain and the paucity of work in healthy infants, this paper highlights key areas for improving parental pain management in primary care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".