Assessing the Landscape in Medical Education Literature in Medical Oncology: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Medical oncology and medical education (ME) have both expanded exponentially over the past 50 years; thus, 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 ME literature in medical oncology to inform future scholarship in this area. METHODS: MEDLINE, Embase, ERIC, and Web of Science were searched to find peer-reviewed English language articles on postgraduate ME in medical oncology published from 2009 to 2020. Established scoping review methodologies were used in study design; articles were classified by specialty, learner training level, region of authorship, institution type, year of publication, journal type, study methodology, and research topic. Curriculum intervention, scholarship, and domain(s) of physician competency were also assessed. The results were interpreted using descriptive statistics and collated using predetermined conceptual frameworks. RESULTS: A total of 2,959 references were initially found across four databases. After title and abstract screening, 305 articles remained; after full-text review, 144 articles were included in final analysis. Postgraduate medical oncology education research is increasing, with the majority of articles published in North America. Quantitative studies were most common, primarily survey approaches. For physician competencies, professionalism and medical expertise comprised the large majority of article focuses, whereas very few articles addressed leadership or health advocacy. Curriculum development, professional development, and communication skills were dominant research themes while no articles discussed teacher training. CONCLUSION: Although areas such as professionalism and communication skills are well-studied, medical oncology ME research is lacking in leadership, health advocacy, and teacher training. This study provides valuable guidance for future ME research in medical oncology and establishes a benchmark to examine changes in educational scholarship over time.
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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 | 0.075 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.088 | 0.066 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".