Changing Healthcare Providers' Behavior during Pediatric Inductions with an Empirically Based Intervention
Bibliographic record
Abstract
BACKGROUND: Each year more than 4 million children experience significant levels of preoperative anxiety, which has been linked to poor recovery outcomes. Healthcare providers (HCPs) and parents represent key resources for children to help them manage their preoperative anxiety. The current study reports on the development and preliminary feasibility testing of a new intervention designed to change HCP and parent perioperative behaviors that have been reported previously to be associated with children's coping and stress behaviors before surgery. METHODS: An empirically derived intervention, Provider-Tailored Intervention for Perioperative Stress, was developed to train HCPs to increase behaviors that promote children's coping and decrease behaviors that may exacerbate children's distress. Rates of HCP behaviors were coded and compared between preintervention and postintervention. In addition, rates of parents' behaviors were compared between those that interacted with HCPs before training to those interacting with HCPs after the intervention. RESULTS: Effect sizes indicated that HCPs who underwent training demonstrated increases in rates of desired behaviors (range: 0.22-1.49) and decreases in rates of undesired behaviors (range: 0.15-2.15). In addition, parents, who were indirectly trained, also demonstrated changes to their rates of desired (range: 0.30-0.60) and undesired behaviors (range: 0.16-0.61). CONCLUSIONS: The intervention successfully modified HCP and parent behaviors. It represents a potentially new clinical way to decrease anxiety in children. A multisite randomized control trial funded by the National Institute of Child Health and Development will examine the efficacy of this intervention in reducing children's preoperative anxiety and improving children's postoperative recovery.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".