Developing a measure of distress-promoting parent behaviors during infant vaccination: Assessing reliability and validity
Bibliographic record
Abstract
Background Infants rely on their parents’ sensitive and contingent soothing to support their regulation from pain-related distress. However, despite being of potentially equal or greater import, there has been little focus on how to measure distress-promoting parent behaviors. Aims The goal of this article was to develop and validate a measure of distress-promoting parent behaviors for acute painful procedures (e.g., vaccinations) that could be used by researchers and clinicians. Methods Following initial generation of measure items, focused group discussions were held with vaccinating clinicians to understand the measure’s face, content, and ecological validity. Archival video footage (n = 537 videos of infant-caregiver dyads during vaccination) was then coded using the measure of distress-promoting behaviors for 3 minutes post vaccine injection. Validity and reliability were examined using correlational analyses. Construct validity was assessed by convergent relationships with infant pain-related distress and divergent relationships were assessed with parent sensitivity and soothing-promoting behaviors. Results The measure demonstrated both moderate to excellent interrater and test-retest reliability and convergent and divergent validity (absolute magnitude of r’s = 0.30 to 0.46). Conclusions By demonstrating strong reliability and validity, this measure represents a promising new way to understand how caregivers interact with infants during painful procedures. Through focusing on distress promotion and using a format that may be coded both from video or in vivo, it is a feasible way to operationalize the impact of the caregiver on the infant’s pain experience in both research and clinical settings.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".