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Record W4229011486 · doi:10.12927/hcq.2022.26806

Key Healthcare Leadership Competencies: Perspectives from Current Healthcare Leaders

2022· article· en· W4229011486 on OpenAlexaffvenue
Gillian Parker, Tina Smith, Christine Shea, Tyrone Perreira, Abi Sriharan

Bibliographic record

VenueHealthcare Quarterly · 2022
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsOntario Medical AssociationKrembil FoundationUniversity of Toronto
Fundersnot available
KeywordsHealth carePublic relationsGovernment (linguistics)Emotional intelligenceHealthcare systemHealth administrationKnowledge managementPsychologyBusinessPolitical science

Abstract

fetched live from OpenAlex

The healthcare system is complex and requires effective leaders who can navigate team, organizational and system dynamics. The objectives of this study were to explore competencies required to lead emerging healthcare challenges and identify strategies for developing successful leaders. Semi-structured interviews were conducted with 12 healthcare leaders from the government, hospitals and in consulting. This study unpacks competencies such as communication and change management and draws attention to the significance of emotional intelligence and working with data that have not traditionally been identified as key competencies. These findings can inform curriculum and modernization initiatives in healthcare leadership programs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.201
GPT teacher head0.401
Teacher spread0.200 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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