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Record W2206143544 · doi:10.1260/2040-2295.6.4.635

Healthcare Engineering Defined: A White Paper

2015· article· en· W2206143544 on OpenAlexaff
Ming‐Chien Chyu, Tony Austin, Fethi Çalışır, Samuel Chanjaplammootil, Mark J. Davis, Jesús Favela, Heng Gan, Amit Gefen, Ram Haddas, Shoshana Hahn‐Goldberg, Roberto Hornero, Yu-Li Huang, Øystein Jensen, Zhongwei Jiang, J.S. Katsanis, Jeong–A Lee, Gladius Lewis, Nigel H. Lovell, Heinz-Theo Luebbers, George G. Morales, Timothy I. Matis, Judith T. Matthews, Łukasz Mazur, E. Y. K. Ng, K. J. Oommen, Kevin Ormand, Tarald Rohde, Daniel Morillo, Justo García‐Sanz‐Calcedo, Mohamad Sawan, Chwan‐Li Shen, Jiann-Shing Shieh, Chao‐Ton Su, Lilly Sun, Mingui Sun, Yi Sun, Senay N. Tewolde, Eric A. Williams, Chongjun Yan, Jiajie Zhang, Yuan‐Ting Zhang

Bibliographic record

VenueJournal of Healthcare Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversité de MontréalPolytechnique MontréalUniversity Health Network
Fundersnot available
KeywordsHealth careWhite paperWhite (mutation)EngineeringEngineering managementComputer scienceData scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

Engineering has been playing an important role in serving and advancing healthcare. The term "Healthcare Engineering" has been used by professional societies, universities, scientific authors, and the healthcare industry for decades. However, the definition of "Healthcare Engineering" remains ambiguous. The purpose of this position paper is to present a definition of Healthcare Engineering as an academic discipline, an area of research, a field of specialty, and a profession. Healthcare Engineering is defined in terms of what it is, who performs it, where it is performed, and how it is performed, including its purpose, scope, topics, synergy, education/training, contributions, and prospects.

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)0.011
metaresearch head score (Gemma)0.013
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 score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.006
Scholarly communication0.0160.010
Open science0.0020.007
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0220.015

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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

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