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Record W2625211195 · doi:10.5489/cuaj.4525

Exploring the business of urology: Change management

2017· article· en· W2625211195 on OpenAlexaffvenue
J. Stuart Oake, Timothy O. Davies, Anne‐Marie Houle, Darren Beiko

Bibliographic record

VenueCanadian Urological Association Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsQueen's UniversityUniversité de MontréalMcMaster UniversityUniversity of Ottawa
Fundersnot available
KeywordsChange management (ITSM)CLARITYRelevance (law)Value (mathematics)Process (computing)ConvictionConsistency (knowledge bases)Social changeEngineering ethicsMedicinePublic relationsPsychologyPolitical scienceComputer scienceBusinessEngineeringMarketing

Abstract

fetched live from OpenAlex

Change is inevitable. All organizations need to change to maintain relevance and to successfully adapt to varying external forces. Change is a challenge for those involved in it. It is the antithesis of consistency and at its core requires a shift in behaviour that is reflexic and instinctual. The authors of this paper would suggest that given the nature of healthcare, its evolving research, its social value/importance, and its cost, that no other profession is more subject to change than medicine. Change is necessary, inevitable, and difficult. A solid understanding of change and its management process is a prerequisite for professional survival. Preparation, conviction, purpose, and clarity are the core values of change. Physicians, more than any other profession, have been driven to and exposed to change. Physicians are in a position to distill its process to a level of understanding that would serve as a model for others to follow. The goals of this paper are to outline the rationale of change, as well as to review change management options and strategies to increase successful change.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.548
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.398
GPT teacher head0.418
Teacher spread0.019 · 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.

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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