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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 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)0.042
metaresearch head score (Gemma)0.127
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.127
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0130.010
Scholarly communication0.0250.025
Open science0.0040.020
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0430.025

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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