Caregiver Ratings of Toddler Pain: The Role of Caregiver Psychological Predictors
Bibliographic record
Abstract
INTRODUCTION/AIM: Young children's limited ability to self-report pain necessitates an understanding of the factors that influence pain ratings. The current paper examines the relative prediction of caregiver psychological factors and toddler pain behaviors on caregiver pain ratings post-vaccination. METHODS: One hundred fifty-six parent-toddler dyads were video recorded during pediatric vaccinations. Child pain behaviors were coded before, during, and after the needle using the Face, Legs, Activity, Cry, Consolability Scale and the Neonatal Facial Coding System). Caregivers rated their child's pain after the needle, reported pre- and post-needle worry during the visit, and completed rating scales assessing other areas of psychological functioning within 2 weeks after the appointment. Regression models were estimated to examine the relative contribution of child and caregiver factors to the prediction of caregiver pain ratings. RESULTS: The regression model predicting caregiver pain ratings from the toddlers' pain-related distress (facial activity immediately after the needle, overall pain-related behavior immediately after, 1-min and 2-min post-needle) and caregiver worry were significant (adjusted R-square = 0.21), with caregiver pre- and post-needle worry being the only significant predictors of caregiver pain ratings. CONCLUSIONS: This study outlines that although child distress behavior remains a significant influence on pain ratings during toddlerhood, when caregiver worry (pre- and post-needle) was entered into the model, they were the only significant predictors of caregiver pain ratings.
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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 | 0.010 |
| 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 | 0.000 |
| 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".