Preliminary development of a measure of parental behavioral responses to everyday pains in young children: the PREP
Bibliographic record
Abstract
Abstract Introduction: Everyday pains are experienced frequently by young children. Parent responses shape how young children learn about and experience pain. However, research on everyday pains in toddlers and preschoolers is scarce, and no self-report measures of parent responses to their child's pain exist for this age group. Objectives: The objective of this study was to develop a preliminary self-report measure of parent behavioral responses to everyday pains in the toddler and preschool years (the PREP) and examine its relationship with child age, sex, and parent and child distress. Methods: Items for the PREP were based on a behavioural checklist used in a past observational study of caregiver responses to toddler's everyday pains. Parents (N = 290; 93% mothers) of healthy children (47.9% boys) between 18 and 60 months (Mage = 34.98 months, SD = 11.88 months) completed an online survey of 46 initial PREP items, demographic characteristics, and their child's typical distress following everyday pains. An exploratory factor analysis was performed on the PREP items that describe observable actions parents may take in response to their young child's everyday pains. Results: The final solution included 10 items across 3 factors: Distract, Physical Soothe, and Extra Attention and explained 60% of the model variance. All PREP subscales were related to child distress; only Physical Soothe and Extra Attention were related to parent distress. Conclusion: This study was a preliminary step in the development and testing of a new self-report measure of parental responses to everyday pains during early childhood.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".