Optimizing Recruitment and Retention in Cancer Clinical Trials in Low-Resource Settings: Barriers and Facilitators From Nigerian Provider's Perspectives
Bibliographic record
Abstract
PURPOSE: The under-representation of African countries in cancer clinical trials continues to widen the cancer health disparity. In this study, we assessed health care workers' perspectives on recruitment and retention in cancer clinical trials in Nigeria. METHODS: This study was a convergent parallel mixed-methods design, using a survey for quantitative analysis and focus group discussions (FGDs) for further qualitative investigation. The health care providers that participated in the study were drawn from the ICON-3 Practice-based Research Network across the six geopolitical zones in Nigeria. RESULTS: Of the 42 providers, 35 completed the survey and 25 participated in the FGDs. The most cited (agreed or strongly agreed) patient-related barriers were lack of understanding of cancer clinical trials (83%), cultural barriers (77%), and lack of financial compensation for study visits (77%). The most cited provider-related barriers were negative attitude of the clinical team (89%), lack of training in good clinical practice (89%), and an overwhelming clinical workload (86%). On trial-related barriers, about 71% agreed or strongly agreed that lack of trial publicity was a barrier. Over 90% of the respondents agreed or strongly agreed that several factors, including the friendliness of the study team (97%) and clarity in the presentation of trial information (97%), are important facilitators. The FGDs unveiled additional themes, including systems-related barriers such as lack of infrastructure, limited research collaboration, and prolonged ethical approval process, and capacity building and community engagement as potential facilitators. CONCLUSION: Our study provides providers' perspectives on the barriers and facilitators to the recruitment and retention of participants in cancer clinical trials in a low-resource setting and highlights the need for culturally appropriate recruitment strategies.
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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 | 0.044 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".