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Record W2605022975

Clinical Simulation: Evaluating the Usability of a Health Information System in a Telenurse Call Centre.

2017· article· en· W2605022975 on OpenAlexaff
Danica Tuden, Elizabeth M. Borycki, André Kushniruk

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsUsabilityHealth careComputer scienceHealth informaticsOrder (exchange)Clinical decision support systemNursingKnowledge managementMedical educationMedicineDecision support systemPublic healthHuman–computer interactionBusiness
DOInot available

Abstract

fetched live from OpenAlex

Telenursing is a specialized subset of nursing practice that has emerged in response to the development of health care technology. Telenurses use health information systems (HIS) as their main source of health information in order to assist callers with their health related concerns on the telephone. In order to ensure that the telenurse-caller encounter supports a safe, appropriate and patient focused outcome, it was important to evaluate the usability of (HIS) that telenurses use. The purpose of this paper is to describe usability issues that emerged during a clinical simulation study of nurses working in a call centre. Clinical simulation was thus chosen as the most comprehensive methodology in this study and its benefits will be discussed.

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)0.021
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.188
GPT teacher head0.472
Teacher spread0.284 · 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 designObservational
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

Citations3
Published2017
Admission routes1
Has abstractyes

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