Tablet-Based Intervention for Reducing Children's Preoperative Anxiety: A Pilot Study
Bibliographic record
Abstract
OBJECTIVES: To examine the feasibility, acceptability, and effects of a novel tablet-based application, Story-Telling Medicine (STM), in reducing children's preoperative anxiety. METHODS: Children (N = 100) aged 7 to 13 years who were undergoing outpatient surgery were recruited from a local children's hospital. This study comprised 3 waves: Waves 1 (n = 30) and 2 (n = 30) examined feasibility, and Wave 3 (n = 40) examined the acceptability of STM and compared its effect on preoperative anxiety to Usual Care (UC). In Wave 3, children were randomly allocated to receive STM+UC or UC. A change in preoperative anxiety was measured using the Children's Perioperative Multidimensional Anxiety Scale (CPMAS) 7 to 14 days before surgery (T1), on the day of surgery (T2), and 1 month postoperatively (T3). RESULTS: Wave 1 demonstrated the feasibility of participant recruitment and data collection procedures but identified challenges with attrition at T2 and T3. Wave 2 piloted a modified protocol that addressed attrition and increased the feasibility of follow-up. In Wave 3, children in the STM+UC demonstrated greater reductions in CPMAS compared with the UC group (ΔM = 119.90, SE = 46.36, t(27) = 2.59, p = .015; 95% confidence interval = 24.78-215.02). CONCLUSION: This pilot study provides preliminary evidence that STM is a feasible and acceptable intervention for reducing children's preoperative anxiety in a busy pediatric operative setting and supports the investigation of a full-scale randomized controlled trial.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".