Survey of Hematology/Oncology Program Leaders on Equity and Global Health Opportunities for Fellows
Bibliographic record
Abstract
PURPOSE: The study assesses the current state of global oncology (GO)/hematology training opportunities in US fellowship programs. METHODS: We developed a comprehensive survey of 64-Likert multiple-choice and open-ended questions. The survey was electronically distributed to fellowship program leaders at Accreditation Council for Graduate Medical Education-accredited adult hematology/oncology fellowships. Program directors received three reminders after which survey was sent to assistant program directors or division heads for programs not represented. RESULTS: A total of 171 programs were eligible for the survey. We received 42 (24.6%) responses; 40 were included in the analysis, and two were excluded for declined consent and incomplete responses. The programs include large academic (81.6%) and community hospitals (10.5%). Of the respondents, 18 (48.6%) reported offering some opportunities for global health training, and half reported interest among current fellows. Most programs (29, 82.9%) had three or fewer faculty engaged in GO research. Institutional training grants were available in 15 (39.5%) programs, of which six (40%) allowed for global health research. Of the 18 programs offering global health training activities, most (15, 83.3%) report less than a quarter of their trainees currently participate in GO experiences. The most commonly perceived barriers to GO opportunities include competing priorities (85.3%) and lack of faculty mentors with GO-related experience (82.4%). Conversely, the most commonly perceived facilitators include established partnerships outside the United States (97.0%) and dedicated institutional funding (93.9%). CONCLUSION: Our survey demonstrates that although there is significant interest among fellowship trainees, a minority of the fellowship programs offer GO opportunities. Providing GO opportunities would require programs to establish partnerships with institutions outside the United States and to have systematic approaches of addressing other barriers, including enhancing funding and mentorship.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".