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Record W4401435063 · doi:10.3389/frym.2024.1376603

Virtual Reality: A Game Changer for Children’s Medical Procedures

2024· article· en· W4401435063 on OpenAlexaff
Peter Joseph Mounsef, Sofia Addab, Reggie C. Hamdy, Sylvie Le May, Kelly Thorstad, Argerie Tsimicalis

Bibliographic record

VenueFrontiers for Young Minds · 2024
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineShriners Hospitals for Children - CanadaUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsDistractionVirtual realityMedicinePsychologyMedical educationComputer scienceHuman–computer interactionCognitive psychology

Abstract

fetched live from OpenAlex

Going to the hospital can be scary for children, especially when they must go through a painful procedure. Doctors, nurses, and other health professionals may use special distraction techniques to help take children’s minds off the pain. There is a cool new way to help distract children from painful procedures. It is called virtual reality (VR). A study was done at a children’s hospital that specializes in bone care. The researchers wanted to know if VR was easy to use at the hospital to help the children deal with pain. The study included 44 children who had different kinds of procedures done, like having a needle put in their vein, removing stitches, having blood taken, and more. The researchers concluded that VR can help. VR is fast and easy to use, almost everyone liked it, and it works. To help with pain relief, virtual reality has all the ingredients to be a recipe for success!

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.004

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 head0.014
GPT teacher head0.294
Teacher spread0.280 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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