Implementation and Evaluation of a Cancer Immunotherapy Elective for Medical Students: Mixed Methods Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Cancer immunotherapy represents a transformative advancement in oncology, offering new avenues for treating malignancies by harnessing the immune system. Despite its growing clinical relevance, immunotherapy remains underrepresented in undergraduate medical education, particularly in curricula integrating foundational immunology with clinical application. To address this gap, we developed and implemented a fully online elective for fourth-year medical students focused on core immunology concepts, immunotherapy mechanisms, FDA-approved treatments, immune-related adverse events, and patient-centered therapeutic decision-making. OBJECTIVE: This study aimed to evaluate the effectiveness of an asynchronous-synchronous online cancer immunotherapy elective in improving medical student knowledge, engagement, and critical-thinking skills. We hypothesized that participation in the elective would be associated with perceived improvements in knowledge and clinical preparedness and inform future strategies for integrating cancer immunotherapy into medical curricula. METHODS: We conducted a mixed-methods study with fourth-year medical students enrolled in a two-week elective at a U.S. medical school. The curriculum included a self-paced foundational module, online discussion board, and a capstone oral presentation requiring students to propose a novel immunotherapy approach. Participants completed pre- and post-course quizzes assessing immunotherapy knowledge and an anonymous post-course Likert-scale survey. Quantitative data were summarized descriptively, and Likert responses were reported using medians and interquartile ranges (IQR). Due to the small sample size, unpaired t-tests comparing pre- and post-course quiz averages were underpowered to detect statistically significant differences. Qualitative data were analyzed using inductive thematic analysis with investigator triangulation. RESULTS: A total of 35 students completed the elective, and 20 submitted the post-course survey (response rate: 57%). Across all Likert-scale items, students reported a median response of 5 (Strongly Agree) with IQR values ranging from 0 to 1, indicating uniformly positive perceptions and minimal variability in their evaluation of the course. Descriptively, average post-course quiz scores were higher than pre-course scores, suggesting improved conceptual understanding. Qualitative thematic analysis revealed three major themes: (1) increased confidence engaging with complex immunotherapy mechanisms, (2) appreciation for the flexibility and interactivity afforded by the hybrid asynchronous-synchronous model, and (3) enhanced understanding of the real-world clinical application of immunotherapy across interdisciplinary settings. CONCLUSIONS: Descriptive quantitative and qualitative findings suggest that a targeted online cancer immunotherapy elective may enhance learners' perceived knowledge and critical-thinking capacity related to emerging cancer therapies. The course's hybrid structure offered flexibility, accessibility, and potential scalability. As immunotherapy continues to expand in clinical practice, this model provides a promising framework for integration into medical curricula. Future work should include larger cohorts and longitudinal follow-up into residency to more rigorously assess educational impact.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".