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Record W2416187532

Development of a Video Coding Scheme for Analyzing the Usability and Usefulness of Health Information Systems.

2015· article· en· W2416187532 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsUsabilityComputer scienceHealth informaticsCoding (social sciences)Usability engineeringHuman–computer interactionUsability inspectionMultimediaWorld Wide WebData sciencePublic healthMedicine
DOInot available

Abstract

fetched live from OpenAlex

Usability has been identified as a key issue in health informatics. Worldwide numerous projects have been carried out in an attempt to increase and optimize health system usability. Usability testing, involving observing end users interacting with systems, has been widely applied and numerous publications have appeared describing such studies. However, to date, fewer works have been published describing methodological approaches to analyzing the rich data stream that results from usability testing. This includes analysis of video, audio and screen recordings. In this paper we describe our work in the development and application of a coding scheme for analyzing the usability of health information systems. The phases involved in such analyses are described.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.102

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.273
Teacher spread0.157 · 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