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Record W4402533813 · doi:10.1177/08404704241279501

How national healthcare change initiatives balance emergent and deliberate change: A principles-focused evaluation

2024· article· en· W4402533813 on OpenAlexafffund
Tavis Apramian, Allia Karim, Kathryn Parker, Lynne Sinclair, Zeenat Ladak, Cheryl Ku, Sarah Gregor, Lily Winnebota, D Ponte, Stella Ng

Bibliographic record

VenueHealthcare Management Forum · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity Health NetworkMcMaster UniversityCanadian Transplant AssociationUniversity of Toronto
FundersEmployment and Social Development CanadaUniversity of TorontoCollege of Family Physicians of Canada
KeywordsTransformational leadershipHealth careProcess (computing)Organizational changeTheory of changeKnowledge managementMedical educationProcess managementPublic relationsEngineering ethicsNursingComputer sciencePolitical scienceMedicineSociologyBusinessEngineering

Abstract

fetched live from OpenAlex

Principles-focused evaluation reflects on the change process itself through examination of its underlying principles. The Centre for Advancing Collaborative Healthcare & Education (CACHE) worked to build interprofessional education programs and tools that attended to the Team Primary Care (TPC) principles. Our internally directed principles-focused evaluation, presented here, asks how CACHE adhered to these principles in the programs and tools it delivered to the TPC project. The article's main contribution is the creation of a new concept, organizational critically reflective practice, which describes an approach health leaders can use to mitigate the limitations of short-term initiatives while pursuing transformational change. We propose specific tools and steps that will help health leaders attempting to enact organizational critically reflective practice.

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)0.228
metaresearch head score (Gemma)0.296
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: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2280.296
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0050.006
Scholarly communication0.0080.010
Open science0.0030.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.212
GPT teacher head0.481
Teacher spread0.269 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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