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Record W7036589186

Clinical Decision Support Systems Continuance: Integrating Physiciansâ Professional Identity with Delone & McLean IS Success Model

2014· article· en· W7036589186 on OpenAlexaff

Bibliographic record

VenueJournal of the Association for Information Systems · 2014
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsContinuanceExtant taxonInformation systemIdentity (music)Information qualityHealth careHealthcare systemManagement information systems
DOInot available

Abstract

fetched live from OpenAlex

Despite the importance of sustained and continued use of healthcare information systems to reap their benefits of cost reductions and quality improvements, research on continuance use of these systems is limited. In this paper, we study physicians’ continuance to use a pain management clinical decision support system by developing a theoretical model that integrates Delone and McLean IS success model with physicians’ professional identity constructs, an integration that addresses the gap of separating information systems constructs from physicians’ idiosyncrasies constructs in extant literature. We conduct our study through a mixed methods longitudinal design that addresses our research questions. This study enhances our understanding of factors influencing physicians’ continuance behavior and extends current literature on healthcare information systems use.

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 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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.006
Open science0.0010.000
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 head0.035
GPT teacher head0.338
Teacher spread0.303 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2014
Admission routes1
Has abstractyes

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