Assessing the landscape in medical oncology medical education scholarship: A scoping study.
Bibliographic record
Abstract
11009 Background: Medical oncology and medical education have both expanded exponentially over the past 50 years; as such, it is important to understand the current status of postgraduate medical oncology education and develop ways to advance this field. This study undertakes a scoping review of medical education literature in medical oncology to inform future scholarship in this area. Methods: MEDLINE (Ovid), Embase (Ovid), ERIC (EBSCO), and Web of Science (UBC Core Collection) were searched to find peer-reviewed English language articles on Postgraduate Medical Education in Medical Oncology published between 2009 and 2020. The review was designed in accordance with updated methodological guidance for the conduct of scoping review. Articles were classified by learning specialty, learner training level, region of authorship, single or multi-institution, year of publication, whether the journal was an education journal, quantitative vs qualitative design, study methodology, and category or topic. A modified Kerns framework for curriculum development was used to assess the type of curriculum intervention, Boyer’s definition of scholarship was used to classify the type of scholarship, and the CanMEDS Framework was used to map the domains of physician competency each study aims to address. Results were interpreted using descriptive statistics and collated and summarized utilizing predetermined conceptual frameworks. Results: 2959 references were initially found across the 4 databases. After title and abstract screening, 305 articles remained; after full text review, a total of 144 articles were included in our final analysis. These data showed that postgraduate medical oncology graduate medical education scholarship is increasing and most commonly observed in the United States. Quantitative studies were most common with surveys used as the most popular study approach. In terms of CanMEDS framework, Professional and Medical Expert comprised the large majority of education focuses, while very few articles addressed Leader or Health Advocate. Curriculum development, professional development, and attitudinal skills (communication skills, ethics) were the dominant research themes, while no articles discussed teacher training. Conclusions: By investigating the body of current literature, this research identifies areas of highest priority in postgraduate medical oncology graduate medical education and opportunities for growth. Whereas areas like professionalism and attitudinal skills are well-studied, research is lacking in leadership, health advocacy and teaching training. This study provides guidance for future medical education scholarship in medical oncology and establishes a benchmark to examine changes in medical oncology educational scholarship over time.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.042 | 0.049 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".