Parental Report of Self and Child Worry During Acute Pain
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine which variables predict parental postvaccination pain ratings. It was hypothesized that after child behavior, parental sensitivity, and parental reports of worry would be the strongest predictors. METHODS: Data for 215 parent-child dyads were analyzed from a longitudinal cohort at the preschool (4 to 5 y of age) vaccination. Preschoolers' pain behaviors 15 seconds, 1 minute 15 seconds, and 2 minutes 15 seconds after the painful immunization were observed and rated. Parental sensitivity, as well as parental own worry and their assessment of their child's worry, were assessed before and after the needle. Three regression models were used to determine the impact of these variables on parental pain assessment. RESULTS: Preschoolers' pain behaviors moderately accounted for variance in parental pain judgment (R=0.23 to 0.28). Parental sensitivity was not a significant unique predictor of parental pain rating at the preschool age. Parental assessment of their own worry and worry about their preschoolers after the needle were critical contributors to parental pain judgment. Post hoc analyses suggest that parents who report low child worry, are more congruent with their child during regulatory phases postvaccination. However, both parents with high and low self-worry had more congruent pain ratings with child pain behavior scores during the reactivity phase. DISCUSSION: The study suggests that the majority of variance in parent pain ratings was not predominantly based on preschoolers' pain behaviors. Parental worry levels and their assessment of their child's worry were also significant predictors. Clinical implications are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".