Predicting preschool pain-related anticipatory distress: the relative contribution of longitudinal and concurrent factors
Bibliographic record
Abstract
Anticipatory distress prior to a painful medical procedure can lead to negative sequelae including heightened pain experiences, avoidance of future medical procedures, and potential noncompliance with preventative health care, such as vaccinations. Few studies have examined the longitudinal and concurrent predictors of pain-related anticipatory distress. This article consists of 2 companion studies to examine both the longitudinal factors from infancy as well as concurrent factors from preschool that predict pain-related anticipatory distress at the preschool age. Study 1 examined how well preschool pain-related anticipatory distress was predicted by infant pain response at 2, 4, 6, and 12 months of age. In study 2, using a developmental psychopathology framework, longitudinal analyses examined the predisposing, precipitating, perpetuating, and present factors that led to the development of anticipatory distress during routine preschool vaccinations. A sample of 202 caregiver-child dyads was observed during their infant and preschool vaccinations (the Opportunities to Understand Childhood Hurt cohort) and was used for both studies. In study 1, pain response during infancy was not found to significantly predict pain-related anticipatory distress at preschool. In study 2, a strong explanatory model was created whereby 40% of the variance in preschool anticipatory distress was explained. Parental behaviours from infancy and preschool were the strongest predictors of child anticipatory distress at preschool. Child age positively predicted child anticipatory distress. This strongly suggests that the involvement of parents in pain management interventions during immunization is one of the most critical factors in predicting anticipatory distress to the preschool vaccination.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".