MétaCan
Menu
Back to cohort
Record W3087777304 · doi:10.1136/leader-2019-000207

Leadership in healthcare: a bibliometric analysis of 100 most influential publications

2020· article· en· W3087777304 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMJ Leader · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsUniversity of British ColumbiaVancouver General HospitalWestern University
Fundersnot available
KeywordsHealth careHealthcare deliveryCitationInclusion (mineral)BibliometricsLeadership developmentMedical educationPsychologyMedicineSociologyPolitical sciencePublic relationsLibrary scienceSocial scienceComputer science

Abstract

fetched live from OpenAlex

Aim We analysed the 100 most influential articles on leadership in healthcare via a bibliometric analysis to better understand categories and topics in leadership science and their relationship to healthcare. Leadership in healthcare is ever evolving and needs to be robust like any another profession. Methods A bibliometric analysis was performed. Articles were ranked by citation counts and three independent reviewers screened the abstracts for inclusion. Common themes were categorised. Results Citations for articles ranged from 53 to 487 and were published across 50 journals. Articles focused primarily on three leadership subjects: team building, quality improvement and healthcare delivery. Of healthcare provider groups, articles were directed to or concerning primarily: nursing, academic medicine and critical care medicine. Conclusions We identified gaps in healthcare leadership development literature. There is an opportunity to effectively identify areas of interest and demand for organised leadership education and training.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0390.225
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.659
GPT teacher head0.546
Teacher spread0.113 · 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