Cancer research in vulnerable populations: A call for collaboration and sustainability from MENATC countries.
Bibliographic record
Abstract
e13649 Background: Cancer is a major burden across the Middle East and North Africa including Turkey and Cyprus (MENATC). Many MENATC countries experience acute and chronic emergencies, including COVID-19 pandemic, disasters, political instability, fragility, as well as chronic conflict that result in cumulative vulnerability to the region. This study examines the current level and the potential for cancer research among vulnerable populations in the MENATC, including its challenges, and gaps. Furthermore, it tries to ascertain cancer practitioners’ views on what defines vulnerability in the MENATC. Methods: Recurrent expert-driven meetings were held to conceptualize the study approach and highlight the barriers to conducting clinical cancer research among vulnerable populations in the MENATC. A self-administered online survey questionnaire was circulated to over 500 cancer practitioners in twenty-three MENATC countries. The survey covered: Demographic and general information, Clinical practice, Research capacity, Vulnerable populations, and logistics. Results: Half of the respondents considered clinical research in vulnerable cancer patients a key concern, while 24.5% did not. Out of the total respondents, 21.8% had worked on research that explicitly included vulnerable populations. About 60% of respondents reported seeing vulnerable populations during their daily practice. Lack of funding (60%), lack of protected time (42%), and lack of research training (35%) were the top three main reasons for research scarcity and major research challenges. Over half of the respondents agreed that recent wars/conflicts worsened the conditions for vulnerable populations. The top five ranked vulnerability groups were geriatric, terminally ill, mental health-related, chronically ill, and socioeconomically deprived patients, while the lower five ranked groups were LGBTQ+ community, veterans, divorced individuals or widowed women, prisoners, and ethnic minorities patients. Conclusions: This is the first study in the MENATC region to look at the status of and potential for research among vulnerable populations. The study highlights the challenges faced by cancer practitioners in the MENATC in research, especially among vulnerable populations. We believe that these limitations in research will negatively impact the outcomes for vulnerable communities in the region. Lack of research funding and training of cancer practitioners in the MENATC are major factors negatively affecting cancer outcomes which could lead to health improvement failure. Geopolitical, economic, and cultural differences clearly define the most vulnerable populations in cancer research. Addressing cancer disparities in the MENATC is a complex and pressing issue. By working together and providing the necessary resources, we can improve cancer research outcomes for the most vulnerable populations in the region.
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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 | 0.123 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".