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Record W4319005745 · doi:10.3390/curroncol30020136

Competency-Based Workforce Development and Education in Global Oncology

2023· review· en· W4319005745 on OpenAlexaffvenue
Nazik Hammad, Ntokozo Ndlovu, Laura M. Carson, Doreen Ramogola‐Masire, Indranil Mallick, Scott Berry, E. Oluwabunmi Olapade‐Olaopa

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsQueen's University
Fundersnot available
KeywordsWorkforceMedicineOncologyContext (archaeology)Health careWork (physics)RealmInternal medicineMedical educationProfessional developmentWorkforce developmentRelevance (law)NursingPolitical science

Abstract

fetched live from OpenAlex

The healthcare workforce plays a pivotal role in cancer care delivery, leadership, policy, education, and research in complex cancer systems. To ensure quality and relevance, health professionals must have the necessary competencies to deliver patient-centered and efficient care, coupled with the ability to work in teams and manage health resources wisely. This paper aims to review the concept of competency-based medical education (CBME) in the context of oncology to provide insights and guidance for those interested in adopting or adapting competency-based education in training programs. The results of a scoping review of CBME in oncology are presented here to describe the current status of CBME in oncology. The literature describing the implementation and evaluation of CBME in oncology training programs for medical professionals internationally is summarized and key themes identified to provide practical guidance for educators. Further, the paper identifies critical competencies for oncology education and training globally and presents recommendations and opportunities for collaboration in competency-based education and training in oncology. The authors argue for increased global collaboration and networking in the realm of CBME to facilitate the establishment of a competent global cancer care workforce.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.228
GPT teacher head0.433
Teacher spread0.205 · 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.

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
Published2023
Admission routes2
Has abstractyes

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