The Effect of Robot-Led Distraction during Needle Procedures on Pain-Related Memory Bias in Children with Chronic Diseases: A Pilot and Feasibility Study
Bibliographic record
Abstract
The current study evaluated the feasibility and preliminary clinical impact of robot-led distraction during needle procedures in children with chronic diseases on pain-related memories. Participants were 22 children (8−12 years old) diagnosed with a chronic disease (e.g., chronic immune deficiency) and undergoing a needle procedure as part of their routine treatment. Children were randomized to the experimental group (i.e., robot-led distraction) or control group (i.e., usual care). For feasibility, we evaluated study- and needle-procedure-related characteristics, intervention fidelity and acceptability, and nurse perceptions of the intervention. Primary clinical outcomes included children’s memory bias for pain intensity and pain-related fear (1 week later). Results indicated that intervention components were >90% successful. Overall, the robot-led distraction intervention was perceived highly acceptable by the children, while nurse perceptions were mixed, indicating several challenges regarding the intervention. Preliminary between-group analyses indicated a medium effect size on memory bias for pain intensity (Hedges’ g = 0.70), but only a very small effect size on memory bias for pain-related fear (Hedges’ g = 0.09), in favor of the robot-led distraction intervention. To summarize, while feasible, certain challenges remain to clinically implement robot-led distraction during needle procedures. Further development of the intervention while accounting for individual child preferences is recommended.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".