A cross-sectional examination of the relationships between caregiver proximal soothing and infant pain over the first year of life
Bibliographic record
Abstract
Although previous research has examined the relationships between caregiver proximal soothing and infant pain, there is a paucity of work taking infant age into account, despite the steep developmental trajectory that occurs across the infancy period. Moreover, no studies have differentially examined the relationships between caregiver proximal soothing and initial infant pain reactivity and pain regulation. This study examined how much variance in pain reactivity and pain regulation was accounted for by caregiver proximal soothing at 4 routine immunizations (2, 4, 6, and 12 months) across the first year of life, controlling for preneedle distress. One latent growth model was replicated at each of the 4 infant ages, using a sample of 760 caregiver-infant dyads followed longitudinally. Controlling for preneedle infant distress, caregiver proximal soothing accounted for little to no variance in infant pain reactivity or regulation at all 4 ages. Preneedle distress and pain reactivity accounted for the largest amount of variance in pain regulation, with this increasing after 2 months. It was concluded that within each immunization appointment across the first year of life, earlier infant pain behaviour is a stronger predictor of subsequent infant pain behaviour than caregiver proximal soothing. Given the longer-term benefits that have been demonstrated for proximal soothing during distressing contexts, caregivers are still encouraged to use proximal soothing during infant immunizations.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".