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Record W3203377115 · doi:10.1016/j.cjco.2021.09.019

Lack of Equity in the Cardiology Physician Workforce: A Narrative Review and Analysis of the Literature

2021· review· en· W3203377115 on OpenAlexaffabout
Michelle Keir, Chanda McFadden, Shannon M. Ruzycki, Sarah Weeks, Michael Slawnych, R. Scott McClure, Vikas Kuriachan, Paul W.M. Fedak, Carlos A. Morillo

Bibliographic record

VenueCJC Open · 2021
Typereview
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsAlberta Health ServicesLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsWorkforceThrivingEquity (law)TerminologyNarrativeDiversity (politics)MedicinePublic relationsHealth equityPopulationPolitical scienceMedical educationSociologyNursingSocial sciencePublic healthLaw

Abstract

fetched live from OpenAlex

The gender and racial diversity in the cardiology workforce in Canada does not reflect that of the population we serve. As social awareness of the principles of equity, diversity, and inclusion rises, our profession must rise to meet the challenges they present. We detail contemporary examples of publication bias in the cardiac sciences literature and describe the factors that led to oversight in the peer-review process. We performed a narrative review to summarize the published literature on equity and diversity among cardiac physicians. We also summarize the challenges faced by women and racial-minority physicians when pursuing and thriving in a career in cardiology, and the systemic barriers to their success. In the past decade, social justice movements have advanced. Professionalism standards are changing, and awareness and understanding of these advances in terminology is imperative for all physicians. In this review, we summarize key language and concepts, with cardiology-specific examples, and propose a new paradigm of professionalism.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.925
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.173
GPT teacher head0.486
Teacher spread0.312 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2021
Admission routes2
Has abstractyes

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