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Record W2119995979 · doi:10.1177/1359105313504794

Humanoid robotics in health care: An exploration of children’s and parents’ emotional reactions

2013· article· en· W2119995979 on OpenAlexaff
Tanya Beran, Otto G. Vanderkooi, Susan Kuhn

Bibliographic record

VenueJournal of Health Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsHumanoid robotDistractionEnthusiasmRobotRoboticsPsychologyHealth careMedicineDevelopmental psychologyApplied psychologyNursingArtificial intelligenceComputer scienceCognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

A new non-pharmacological method of distraction was tested with 57 children during their annual flu vaccination. Given children's growing enthusiasm for technological devices, a humanoid robot was programmed to interact with them while a nurse administered the vaccination. Children smiled more often with the robot, as compared to the control condition, but they did not cry less. Parents indicated that their children held stronger memories for the robot than for the needle, wanted the robot in the future, and felt empowered to cope. We conclude that children and their parents respond positively to a humanoid robot at the bedside.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.118
GPT teacher head0.472
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations75
Published2013
Admission routes1
Has abstractyes

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