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Record W2963606115 · doi:10.1177/0844562119862742

Children’s Fear and Pain During Medical Procedures: A Quality Improvement Study With a Humanoid Robot

2019· article· en· W2963606115 on OpenAlexaffvenue
Christian E. Farrier, Jacqueline D. R. Pearson, Tanya Beran

Bibliographic record

VenueCanadian Journal of Nursing Research · 2019
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsDistractionPhysical therapyIntervention (counseling)Humanoid robotMedicineVisual analogue scaleCoping (psychology)Physical medicine and rehabilitationPsychologyClinical psychologyNursingRobotComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.392
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2019
Admission routes2
Has abstractyes

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