Children’s Fear and Pain During Medical Procedures: A Quality Improvement Study With a Humanoid Robot
Bibliographic record
Abstract
Background Pediatric patients undergo a variety of painful medical procedures. Purpose The goal of this quality improvement study was to introduce a humanoid robot (MEDi®) programmed with strategies, such as distraction and deep breathing, at inpatient and outpatient units to determine any preliminary effects on children’s pain and fear during medical procedures. Methods A nonrandomized two-group pre- and posttest design was used to compare pain and fear of children before and after intervention versus standard care. A total of 46 children aged 2–15 years undergoing various medical procedures in a pediatric hospital, and their parents completed the Children’s Fear Scale and the Faces Pain Scale-Revised. The former was used both before and after the procedure, while the latter only after the procedure. Results Children ( n = 18), who interacted with the robot before and during a procedure, and their parents reported significantly lower levels of fear and pain than did children ( n = 28) and their parents in standard care, ps < .05. Conclusions The use of a humanoid robot programmed with psychological strategies to support coping may enhance children’s experiences of care for pain management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".