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Record W2174727594 · doi:10.12927/hcq..18493

Implementing MOE/MAR: Balancing Project Management with Change

2006· article· en· W2174727594 on OpenAlexaffabout
Stephanie Saull-McCaig, RoseAnn Pacheco, Pakizah Kozak, Susan A. Gauthier, Rebecca Hahn

Bibliographic record

VenueHealthcare Quarterly · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsChange management (ITSM)Best practiceHealth administrationProject managementProgram managementAdministration (probate law)BusinessChange orderProcess managementOperations managementOrder (exchange)MedicineNursingOPM3ManagementEngineeringPolitical sciencePublic health

Abstract

fetched live from OpenAlex

When Toronto-based University Health Network initiated its Medication Order Entry/Medication Administration Record project in 2001, it was well understood by the organization that this would be one of the largest change management initiatives undertaken, and would require rigorous project management, significant clinical involvement and a well-developed change management program.

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)0.239
metaresearch head score (Gemma)0.231
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.239
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2390.231
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0080.007
Scholarly communication0.0190.011
Open science0.0050.016
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.002

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 head0.096
GPT teacher head0.434
Teacher spread0.338 · 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.

Study designQualitative
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

Citations1
Published2006
Admission routes2
Has abstractyes

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