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Record W2419138274 · doi:10.1108/lhs-05-2016-0019

Leadership training for undergraduate medical students

2016· article· en· W2419138274 on OpenAlexaffabout
Victor Maddalena

Bibliographic record

VenueLeadership in health services · 2016
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTraining (meteorology)Medical educationPsychologyManagementMedicine

Abstract

fetched live from OpenAlex

Purpose Physicians play an important leadership role in the management and governance of the healthcare system. Yet, many physicians lack formal management and leadership training to prepare them for this challenging role. This Viewpoint article argues that leadership concepts need to be introduced to undergraduate medical students early and throughout their medical education. Design/methodology/approach Leadership is an integral part of medical practice. The recent inclusion of "Leader" competency in the CanMEDS 2015 represents a subtle but important shift from the previous "manager" competency. Providing medical students with the basics of leadership concepts early in their medical education allows them to integrate leadership principles into their professional practice. Findings The Faculty of Medicine at the Memorial University of Newfoundland (MUN) has developed an eight-module, fully online Physician Leadership Certificate for their undergraduate medical education program. This program is cited as an example of an undergraduate medical curriculum that offers leadership training throughout the 4 years of the MD program. Originality/value There are a number of continuing professional development opportunities for physicians in the area of management and leadership. This Viewpoint article challenges undergraduate medical education programs to develop and integrate leadership training in their curricula.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0360.010

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

Citations34
Published2016
Admission routes2
Has abstractyes

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