Barriers and Challenges to Global Clinical Cancer Research
Bibliographic record
Abstract
BACKGROUND: There are concerns about growing barriers to cancer research. We explored the characteristics of and barriers to global clinical cancer research. METHODS: The American Society of Clinical Oncology International Affairs Committee invited 300 selected oncologists with research experience from 25 countries to complete a Web-based survey. Fisher's exact test was used to compare answers between participants from high-income countries (HICs) and low- and middle-income countries (LMICs). Barriers to clinical cancer research were ranked from 1 (most important) to 8 (least important). Mann-Whitney's nonparametric test was used to compare the ranks describing the importance of investigated obstacles. RESULTS: Eighty oncologists responded, 41 from HICs and 39 from LMICs. Most responders were medical oncologists (62%) at academic hospitals (90%). Researchers from HICs were more involved with academic and industry-driven research than were researchers from LMICs. Significantly higher proportions of those who considered their ability to conduct academic research and industry-driven research over the past 5 years more difficult were from HICs (73% vs. 27% and 70% vs. 30%, respectively). Concerning academic clinical cancer research, a lack of funding was ranked the most important (score: 3.16) barrier, without significant differences observed between HICs and LMICs. Lack of time or competing priorities and procedures from competent authorities were the second most important barriers to conducting academic clinical research in HICs and LMICs, respectively. CONCLUSION: Lack of funding, lack of time and competing priorities, and procedures from competent authorities might be the main global barriers to academic clinical cancer research.
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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 | 0.076 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".