Understanding the Relative Contributions of Sensitive and Insensitive Parent Behaviors on Infant Vaccination Pain
Bibliographic record
Abstract
Parents play a critical role in supporting infants’ ability to manage strong emotions. Routine vaccinations provide an ideal context to observe the effect of parents’ behaviors on infants’ pain-related distress. Previous research in the vaccination context showed that parent sensitivity, operationalized by variables such as emotional availability and proximal soothing behaviors, is associated with infant pain-related distress behavior. However, the magnitudes of these relationships were smaller than expected given the established importance of parents in the development of distress regulation. In recent work, a reliable and valid measure to operationalize insensitive behaviors was developed. The objective of the current study was to examine the relative contribution of variables representing sensitive and insensitive behaviors to the prediction of infant pain-related distress behaviors during the reactivity and regulation phases of needle pain. Archival data was used to analyze a subsample of infants followed during their two-month, six-month, and 12-month vaccinations (n = 81). Results of regression analyses indicated that parent insensitive behaviors generally had the strongest relationships with pain outcomes across all ages, with a greater influence on regulation-phase pain-related distress behavior, rather than reactivity-phase pain-related distress behavior. Our findings support the utility of a measure of distress-promoting parent behaviors in a vaccination context, and highlight the potential value of this measure for clinicians and researchers.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".