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Record W109952292 · doi:10.14236/jhi.v19i3.810

Understanding end-user support for health information technology: atheoretical framework

2011· article· en· W109952292 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

VenueJournal of Innovation in Health Informatics · 2011
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHealth informaticsDecision support systemComputer scienceKnowledge managementInformation systemInformaticsClinical decision support systemEnd userHealth information technologyProcess managementData scienceHealth careWorld Wide WebEngineeringData mining

Abstract

fetched live from OpenAlex

BACKGROUND: Support is often considered an important factor for successful implementation and realising the benefits of health information technology (HIT); however, there is a dearth of research on support and theoretical frameworks to characterise it. OBJECTIVE: To develop and present a comprehensive, holistic, framework for characterising enduser support that can be applied to various settings and types of information systems. METHOD: Scoping review of the medical informatics and information systems literature. RESULTS: A theoretical framework of end-user support is presented. It includes the following facets: support source, location of support, support activities, and perceived characteristics of support and support personnel. CONCLUSION: The proposed framework may be a useful tool for describing and characterising enduser support for HIT. it may also be used by decision makers and implementation leaders for planning purposes.

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)0.022
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.196
GPT teacher head0.456
Teacher spread0.260 · 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