Redefining Cancer Research Priorities in Low- and Middle-Income Countries in the Post–COVID-19 Global Context: A Modified Delphi Consensus Process
Bibliographic record
Abstract
PURPOSE The post–COVID-19 funding landscape for cancer research globally has become increasingly challenging, particularly in resource-challenged regions (RCRs) lacking strong research ecosystems. We aimed to produce a list of priority areas for cancer research in countries with limited resources, informed by researchers and patients. METHODS Cancer experts in lower-resource health care systems (as defined by the World Bank as low- and middle-income countries; N = 151) were contacted to participate in a modified consensus-seeking Delphi survey, comprising two rounds. In round 1, participants (n = 69) rated predetermined areas of potential research priority (ARPs) for importance and suggested missing ARPs. In round 2, the same participants (n = 49) rated an integrated list of predetermined and suggested ARPs from round 1, then undertook a forced choice priority ranking exercise. Composite voting scores ( T-scores) were used to rank the ARPs. Importance ratings were summarized descriptively. Findings were discussed with international patient advocacy organization representatives. RESULTS The top ARP was research into strategies adapting guidelines or treatment strategies in line with available resources (particularly systemic therapy) ( T = 83). Others included cancer registries ( T = 62); prevention ( T = 52); end-of-life care ( T = 53); and value-based and affordable care ( T = 51). The top COVID-19/cancer ARP was strategies to incorporate what has been learned during the pandemic that can be maintained posteriorly ( T = 36). Others included treatment schedule interruption ( T = 24); cost-effective reduction of COVID-19 morbidity/mortality ( T = 19); and pandemic preparedness ( T = 18). CONCLUSION Areas of strategic priority favored by cancer researchers in RCRs are related to adaptive treatment guidelines; sustainable implementation of cancer registries; prevention strategies; value-based and affordable cancer care; investments in research capacity building; epidemiologic work on local risk factors for cancer; and combatting inequities of prevention and care access.
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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 | 0.348 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".