Barriers to a Career Focus in Cancer Prevention: A Report and Initial Recommendations From the American Society of Clinical Oncology Cancer Prevention Workforce Pipeline Work Group
Bibliographic record
Abstract
PURPOSE: To assist in determining barriers to an oncology career incorporating cancer prevention, the American Society of Clinical Oncology (ASCO) Cancer Prevention Workforce Pipeline Work Group sponsored surveys of training program directors and oncology fellows. METHODS: Separate surveys with parallel questions were administered to training program directors at their fall 2013 retreat and to oncology fellows as part of their February 2014 in-training examination survey. Forty-seven (67%) of 70 training directors and 1,306 (80%) of 1,634 oncology fellows taking the in-training examination survey answered questions. RESULTS: Training directors estimated that ≤ 10% of fellows starting an academic career or entering private practice would have a career focus in cancer prevention. Only 15% of fellows indicated they would likely be interested in cancer prevention as a career focus, although only 12% thought prevention was unimportant relative to treatment. Top fellow-listed barriers to an academic career were difficulty in obtaining funding and lower compensation. Additional barriers to an academic career with a prevention focus included unclear career model, lack of clinical mentors, lack of clinical training opportunities, and concerns about reimbursement. CONCLUSION: Reluctance to incorporate cancer prevention into an oncology career seems to stem from lack of mentors and exposure during training, unclear career path, and uncertainty regarding reimbursement. Suggested approaches to begin to remedy this problem include: 1) more ASCO-led and other prevention educational resources for fellows, training directors, and practicing oncologists; 2) an increase in funded training and clinical research opportunities, including reintroduction of the R25T award; 3) an increase in the prevention content of accrediting examinations for clinical oncologists; and 4) interaction with policymakers to broaden the scope and depth of reimbursement for prevention counseling and intervention services.
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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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".