Understanding the concurrent and predictive relations between child-led emotion regulation behaviors and pain during vaccination in toddlerhood
Bibliographic record
Abstract
ABSTRACT: The purpose of this study was to further our understanding of early childhood pain-related distress regulation. Concurrent and predictive relations between child-led emotion regulation (ER) behaviors and pain-related distress during vaccination were examined at 2 different ages using autoregressive cross-lagged path analyses. Toddlers were video-recorded at the 12- and 18-month routine vaccination appointments (12-month-old [N = 163]; 18-month-old [N = 149]). At 1, 2, and 3 minutes postneedle, videos were coded for 3 clusters of child-led ER behaviors (disengagement of attention, parent-focused behaviors, and physical self-soothing) and pain-related distress. The concurrent and predictive relations between child-led ER behaviors and pain-related distress behaviors were assessed using 6 models (3 emotion regulation behaviors by 2 ages). At 18 months, disengagement of attention was significantly negatively related to pain-related distress at 1 minute postneedle, and pain-related distress at 1 minute postneedle was significantly related to less disengagement of attention at 2 minutes postneedle. Parent-focused behaviors had significant positive relations with pain-related distress at both ages, with stronger magnitudes at 18 months. Physical self-soothing was significantly related to less pain-related distress at both ages. Taken together, these findings suggest that disengagement of attention and physical self-soothing may serve more of a regulatory function during toddlerhood, whereas parent-focused behaviors may serve more of a function of gaining parent support for regulation. This study is the first to assess these relations during routine vaccination in toddlerhood and suggests that toddlers in the second year of life are beginning to play a bigger role in their own regulation from painful procedures than earlier in infancy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".