Cancer Research in Vulnerable Populations: A Call for Collaboration and Sustainability From MENAT Countries
Bibliographic record
Abstract
PURPOSE: Cancer is a major burden across Middle East, North Africa, Türkiye (MENAT). Many MENAT countries experience multiple conflicts that compound vulnerabilities, but little research investigates the linkages between vulnerability and cancer research. This study examines the current level and the potential for cancer research among vulnerable populations in the MENAT region, aiming to provide direction toward developing a research agenda on the region's vulnerable populations. METHODS: Expert-driven meetings were arranged among the 10 authors. After obtaining institutional review board approval, a self-administered online survey questionnaire was circulated to more than 500 cancer practitioners working in 22 MENAT countries. RESULTS: Two hundred sixteen cancer practitioners across the MENAT region responded. Fifty percent of the respondents identified clinical research in vulnerable patients with cancer as a significant issue; 21.8% reported previous research experience that included vulnerable populations, and 60% reported encountering vulnerable populations in their daily clinical practice. The main barriers to conducting research were lack of funding (60%), protected time (42%), and research training (35%). More than half of the respondents believed that wars/conflicts constituted an important source of vulnerability. The most vulnerable cancer populations were the elderly, palliative/terminally ill, those with concomitant mental health-related issues, those with other chronic illnesses, and socioeconomically deprived patients. CONCLUSION: Results support that a major effort is needed to improve cancer research among vulnerable cancer populations in the MENAT region. We call for interdisciplinary research that accounts for the region's unique, compounding, and cumulative forms of vulnerability. This cancer research agenda on different vulnerable populations must balance sociobehavioral studies that explore sociopolitical barriers to quality care and clinical studies that gauge and refine treatment protocols. Building a research agenda through collaboration and solidarity with international partners is prime time.
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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 | 0.083 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".