A Survey of Medical Oncology Training in Australian Medical Schools: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Oncology is a rapidly evolving field with continuous advancements in the diagnosis and treatment of cancer. Therefore, it is important that medical students are provided with the knowledge and experience required to care for oncology patients and enable them to diagnose and manage toxicities of novel therapeutic agents. OBJECTIVE: This study was performed to understand the medical students' perspective of the oncology education provided in universities across Australia and identify areas of education that could potentially be modified or improved to ultimately attract more students to a career in oncology. METHODS: This pilot cross-sectional study consisted of an 18-question survey that was submitted online to medical students in their final year and interns rotating to the Tamworth Hospital. RESULTS: The survey was completed by 94 fifth-year medical students and interns. Oncology was taught both theoretically and clinically for 68% (63/93) of participants, and 48% (44/92) had an exclusive oncology rotation. Both theoretical and clinical oncology assessments were conducted for only 21% (19/92) of participants. Overall, 42% (38/91) of participants were satisfied with their oncology education, and 78% (40/51) were dissatisfied with the number of oncology teaching hours. The importance of a career in oncology was rated as low by 46% (41/90) of participants. CONCLUSIONS: This pilot study indicates that there are potential areas to improve oncology teaching in Australian universities. The majority of surveyed students were dissatisfied with the number of teaching hours they receive in oncology. More global assessment of students and/or interns from other Australian institutes may yield further useful information.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".