Trajectories of distress regulation during preschool vaccinations: child and caregiver predictors
Bibliographic record
Abstract
ABSTRACT: Recent research has highlighted the need for a deeper understanding of the heterogeneity in trajectories of children's distress after acute pain exposure, moving beyond the group means of behavioural pain scores at a single timepoint. During preschool vaccinations, 3 distinct trajectories of postvaccination pain regulation have been elucidated, with approximately 75% of children displaying trajectories characterized by downregulation to no distress by 2 minutes postneedle and 25% concerningly failing to downregulate by 2 minutes. The objective of this study was to examine child and caregiver predictors of preschool children's postvaccination regulatory patterns. Our results indicated that higher child baseline distress, more caregiver coping-promoting verbalizations in the first minute after the needle, less coping-promoting verbalizations in the second minute, and more caregiver distress-promoting verbalizations in the second minute after the needle were associated with membership in the trajectories characterized by high distress. Furthermore, although all children's pain-related distress at various timepoints throughout the appointment was most strongly predicted by previous pain scores, different patterns of associations emerged depending on the trajectory exhibited. This research highlights both the need to minimize distress before the needle to avoid the highly distressed trajectory and the importance of considering the heterogeneity of trajectories of preschool pain responding when examining the factors that are associated with children's pain-related distress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".