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Record W2802626419 · doi:10.1108/lhs-09-2017-0058

Turning the Titanic: physicians as both leaders and managers in healthcare reform

2018· article· en· W2802626419 on OpenAlexaffabout
Colleen Grady, C. R. Hinings

Bibliographic record

VenueLeadership in health services · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Change and Leadership
Canadian institutionsUniversity of AlbertaQueen's University
Fundersnot available
KeywordsViewpointsHealth careHealthcare systemConceptual frameworkPublic relationsHealth care reformAffect (linguistics)PsychologyBusinessKnowledge managementPolitical scienceSociologyHealth policyComputer science

Abstract

fetched live from OpenAlex

PURPOSE: Physicians are instrumental in healthcare reform and their capacity to employ both leadership and management skills can affect change at all levels. This paper aims to present the challenges and opportunities for physicians in influencing system change and discuss how the two different but complementary skill sets may enable them to contribute to transformation of healthcare. DESIGN/METHODOLOGY/APPROACH: This is a conceptual paper and represents the viewpoints of both authors while incorporating current evidence through the literature. FINDINGS: Healthcare reform is important and underway in many Canadian provinces, yet it is difficult to achieve change. Leadership and management skills differ although these differences are often subtle in language. Physicians both lead and manage in the healthcare system; their capacity to do both is an advantage for healthcare reform. ORIGINALITY/VALUE: This paper represents the opinions of both authors and is considered original as a conceptual paper.

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)0.025
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.174
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.049
Scholarly communication0.0150.010
Open science0.0010.008
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.091
GPT teacher head0.288
Teacher spread0.197 · 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 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

Citations8
Published2018
Admission routes2
Has abstractyes

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