Preschool children's coping responses and outcomes in the vaccination context: child and caregiver transactional and longitudinal relationships
Bibliographic record
Abstract
This article, based on 2 companion studies, presents an in-depth analysis of preschoolers coping with vaccination pain. Study 1 used an autoregressive cross-lagged path model to investigate the dynamic and reciprocal relationships between young children's coping responses (how they cope with pain and distress) and coping outcomes (pain behaviors) at the preschool vaccination. Expanding on this analysis, study 2 then modeled preschool coping responses and outcomes using both caregiver and child variables from the child's 12-month vaccination (n = 548), preschool vaccination (n = 302), and a preschool psychological assessment (n = 172). Summarizing over the 5 path models and post hoc analyses over the 2 studies, novel transactional and longitudinal pathways predicting preschooler coping responses and outcomes were elucidated. Our research has provided empirical support for the need to differentiate between coping responses and coping outcomes: 2 different, yet interrelated, components of "coping." Among our key findings, the results suggest that a preschooler's ability to cope is a powerful tool to reduce pain-related distress but must be maintained throughout the appointment; caregiver behavior and poorer pain regulation from the 12-month vaccination appointment predicted forward to preschool coping responses and/or outcomes; robust concurrent relationships exist between caregiver behaviors and both child coping responses and outcomes, and finally, caregiver behaviors during vaccinations are not only critical to both child pain coping responses and outcomes in the short- and long-term but also show relationships to broader child cognitive abilities as well.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".