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Beyond the White Coat

2025· book-chapter· en· W4407035247 on OpenAlexaff
Khushi, Jaspreet Kaur, Harwinder Singh

Bibliographic record

VenueAdvances in business strategy and competitive advantage book series · 2025
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsYork University
Fundersnot available
KeywordsWhite coatCoatWhite (mutation)BiologyMaterials scienceComposite materialGenetics

Abstract

fetched live from OpenAlex

In an era where technology increasingly intersects with healthcare, “Beyond the White Coat: A Journey into Virtual Reality Healthcare Innovations” explores the transformative potential of virtual reality (VR) in medical practice. This study delves into the multifaceted applications of VR, from immersive training environments for medical professionals to enhanced patient care and therapeutic interventions. By examining current advancements, case studies, and ongoing research, the study highlights how VR is reshaping diagnostics, treatment planning, and patient engagement. The discussion extends to the challenges and ethical considerations of integrating VR into healthcare, ultimately envisioning a future where virtual experiences are seamlessly integrated into medical practice, enhancing both efficacy and empathy in patient care.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0100.010
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0300.008

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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2025
Admission routes1
Has abstractyes

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