Caregiver and Child Distress as Predictors of Dyadic Physiological Attunement During Vaccination
Bibliographic record
Abstract
OBJECTIVE: Previous research discerned 3 groups of caregiver-toddler dyads that differed in their physiological coregulatory patterns, also known as physiological attunement, during routine vaccinations in the second year of life. One group of dyads (80% of sample) displayed an attuned regulatory pattern, and 2 groups of dyads (20% of sample) showed maladaptive attunement patterns (ie, a lack of attunement or misattunement). The objective of the current study was to examine how well the pain-related distress of children and caregivers during vaccination predicted these patterns. METHODS: Caregiver-toddler dyads (N = 189) were part of a longitudinal cohort observed at either 12-, 18-, or 24-month vaccination appointments. The caregiver's self-report of worry was assessed before and after the needle, and the child behavioral pain-related distress was also measured during the vaccination appointment. Logistic regression was used to determine how well these variables predicted caregiver-child physiological attunement patterns, as indexed by high-frequency heart rate variability. RESULTS: Higher behavioral pain-related distress at various timepoints after the needle were associated with membership in the dyad groups that showed misattunement or lack of attunement. Further, caregivers with higher preneedle worry and lower postneedle worry had a greater likelihood of belonging to groups that showed a maladaptive attunement pattern. DISCUSSION: Findings suggest that caregivers who experience distress associated with their toddlers' vaccination experience more difficulty coregulating with their child during vaccination, and these children are at risk of experiencing higher levels of pain-related distress. This research highlights the need to help caregivers support their children's regulation during vaccination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".