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A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

2008· review· en· W2484605039 on OpenAlexaff
Morgan Price

Bibliographic record

VenueIGI Global eBooks · 2008
Typereview
Languageen
FieldComputer Science
TopicUsability and User Interface Design
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsUsabilityComputer scienceHealth careFocus (optics)Human–computer interactionData scienceInformation systemCognitionManagement scienceKnowledge managementPsychologyEngineeringNeuroscience

Abstract

fetched live from OpenAlex

The purpose of this chapter is to provide the reader with an overview of several models and theories from the general HCI literature, highlighting models at three levels of focus: biomechanical interactions, individual-cognitive interactions, and social interactions. This chapter will also explore how these models were or could be applied to the design and evaluation of clinical information systems, such as electronic medical records and hospital information systems. Finally, it will conclude with how an understanding at each level compliments the other two in order to create a more complete understanding of the interactions of information systems in healthcare.

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)0.008
metaresearch head score (Gemma)0.021
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: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0110.012
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.101
GPT teacher head0.429
Teacher spread0.328 · 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
GenreReview

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
Published2008
Admission routes1
Has abstractyes

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