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Record W2604387476 · doi:10.3233/978-1-61499-742-9-6

The Connecting South West Ontario (cSWO) Benefits Model: An Approach for the Collaborative Capture of Value of Electronic Health Records and Enabling Technology

2017· article· en· W2604387476 on OpenAlexaffabout
Ted Alexander, Lori-Anne Huebner, Mohamed Alarakhia, Kirk Hollohan

Bibliographic record

VenueStudies in health technology and informatics · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsAnalyticsHarmony (color)Value (mathematics)Knowledge managementComputer scienceHealth recordsProcess managementData scienceBusinessHealth carePolitical science

Abstract

fetched live from OpenAlex

This paper explains the benefits model developed and deployed by the connecting South West Ontario (cSWO) program. The cSWO approach is founded on the principles of enabling clinical and organizational value and the recognition that enabling requires a collaborative approach that can include several perspectives. We describe our approach which is aimed at creating a four-part harmony between change management and adoption, best practice research and quality indicators, data analytics and clinical value production.

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)0.010
metaresearch head score (Gemma)0.019
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: none
Teacher disagreement score0.956
Threshold uncertainty score0.824

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.006
Science and technology studies0.0070.008
Scholarly communication0.0100.009
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.194
GPT teacher head0.479
Teacher spread0.286 · 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 routes2
Has abstractyes

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