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Record W3203606212 · doi:10.1177/08404704211040817

A new era of health leadership

2021· article· en· W3203606212 on OpenAlexaff
Kevin Smith, Megha Bhavsar

Bibliographic record

VenueHealthcare Management Forum · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsServant leadershipHierarchyPublic relationsCivilityValue (mathematics)Political scienceEmpowermentSociologyManagementLeadership stylePoliticsLawComputer science

Abstract

fetched live from OpenAlex

Traditional models of health leadership are characterized by top-down structures dependent on hierarchy - which emerged historically from military models. With supporting evidence, many of today's leaders are now working hard to shift their organizations to models of empowered teams and servant leadership with the hopes of inciting a broader cultural shift. The concern is that these early signs of progress could unravel due to the many challenges now exacerbated by COVID-19 and its implications. One such example is fostering respect and civility (i.e. the pillars of empowerment and servant leadership) which is placed at risk during times of change and crisis - more so during a pandemic when command-and-control structures are deemed necessary. The evolution of modern health leadership must be implemented with plans for mitigating related risks. Ultimately, the behaviours that are tolerated during times of stress are what become the value system of any organization.

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)0.006
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.021
Scholarly communication0.0120.013
Open science0.0010.009
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0200.003

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 head0.153
GPT teacher head0.439
Teacher spread0.286 · 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".

Quick stats

Citations13
Published2021
Admission routes1
Has abstractyes

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