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Record W2327820228 · doi:10.1097/ncm.0b013e3181e9225a

How Case Management Leaders Can Succeed With Information Technology

2010· article· en· W2327820228 on OpenAlexaff
Alan E. Cudney

Bibliographic record

VenueProfessional Case Management · 2010
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsImpact
Fundersnot available
KeywordsVendorTransformational leadershipHealth careQuality managementPublic relationsWork (physics)BusinessManagementPsychologyNursingMedicinePolitical scienceOperations managementEngineeringManagement systemMarketing

Abstract

fetched live from OpenAlex

Alan Cudney, RN, CPHQ, PMP, FACHE, is President of HealthCare Impact, LLC. Mr. Cudney is a transformational healthcare leader with extensive experience in clinical IT adoption, case management, disease management, and quality improvement. HealthCare Impact provides experienced management consulting that is making healthcare work better, smarter and faster. Email at [email protected] Address correspondence to Alan Cudney, RN, CPHQ, PMP, FACHE, 4111 Deerfield Dr NW, Concord, North Carolina 20827 ([email protected]). Although Alan works for a vendor, this article is intended to support, clinical improvement and is not a vendor message or solicitation.

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)0.000
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: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.019
GPT teacher head0.324
Teacher spread0.305 · 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 designOther design
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
Published2010
Admission routes1
Has abstractyes

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