Children’s Perioperative Multidimensional Anxiety Scale (CPMAS): Development and validation.
Bibliographic record
Abstract
Up to 5 million children are affected by perioperative anxiety in North America each year. High perioperative anxiety is predictive of numerous adverse emotional and behavioral outcomes in youth. We developed the Children's Perioperative Multidimensional Anxiety Scale (CPMAS) to address the need for a simple, age-appropriate self-report measure of pediatric perioperative anxiety in busy hospital settings. The CPMAS is a visual analog scale composed of 5 items, each of which is scored from 0-100. The objective of this study was to assess the psychometric properties of the CPMAS in children undergoing surgery. Eighty children aged 7 to 13 years who were undergoing elective surgery at a university-affiliated children's hospital were recruited. Children self-completed the CPMAS and the Screen for Childhood Anxiety Related Disorders (SCARED-C) at 3 time points: at preoperative assessment (T1), on the day of the operation (T2), and 1 month postoperatively (T3). Internal consistency, test-retest reliability, and the convergent validity of the CPMAS were assessed across all 3 visits. The CPMAS demonstrated good internal consistency (Cronbach's alpha ≥ .80) and stability (ICC = 0.71) across all 3 visits. CPMAS scores were moderately correlated with total SCARED-C scores (r values = .35 to .54, p values < .05 to .01) and SCARED-C state-related anxiety scores (r values = .29 to .71, p values < .05 to .01) at all 3 time points, suggesting the CPMAS and SCARED-C measures tap similar but not identical phenomena. These results suggest that the CPMAS has the potential to be a useful tool for evaluating perioperative anxiety in children undergoing surgery. (PsycINFO Database Record
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".