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Record W4308183710 · doi:10.3233/shti220992

What Can We Learn from Quality Requirements in ISO/TS 82304-2 for Evaluating Conversational Agents in Healthcare?

2022· article· en· W4308183710 on OpenAlexaff
Kerstin Denecke, Elizabeth M. Borycki, André Kushniruk

Bibliographic record

VenueStudies in health technology and informatics · 2022
Typearticle
Languageen
FieldComputer Science
TopicText Readability and Simplification
Canadian institutionsUniversity of Victoria
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsUsabilityReadabilityComputer scienceQuality (philosophy)Set (abstract data type)Relevance (law)Health careWork (physics)Process managementRisk analysis (engineering)Knowledge managementHuman–computer interactionEngineeringBusiness

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.241
metaresearch head score (Gemma)0.434
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.241
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2410.434
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0140.008
Science and technology studies0.0030.010
Scholarly communication0.0240.025
Open science0.0070.008
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0030.002

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 head0.270
GPT teacher head0.469
Teacher spread0.199 · 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.

Study designTheoretical or conceptual
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

Citations4
Published2022
Admission routes1
Has abstractyes

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