Including migrant oncology patients in research: A multisite pilot randomised controlled trial testing consultation audio-recordings and question prompt lists
Bibliographic record
Abstract
Background: Oncology patients who are migrants or refugees face worse outcomes due to language and communication barriers impacting care. Interventions such as consultation audio-recordings and question prompt lists may prove beneficial in mediating communication challenges. However, designing robust research inclusive of patients who do not speak English is challenging. This study therefore aimed to: a) pilot test and assess the appropriateness of the proposed research design and methods for engaging migrant populations, and b) determine whether a multi-site RCT efficacy assessment of the communication intervention utilising these methods is feasible. Methods: This study is a mixed-methods parallel-group, randomised controlled feasibility pilot trial. Feasibility outcomes comprised assessment of: i) screening and recruitment processes, ii) design and procedures, and iii) research time and costing. The communication intervention comprised audio-recordings of a key medical consultation with an interpreter, and question prompt lists and cancer information translated into Arabic, Greek, Traditional, and Simplified Chinese. Results: Assessment of feasibility parameters revealed that despite barriers, methods utilised in this study supported the inclusion of migrant oncology patients in research. A future multi-site RCT efficacy assessment of the INFORM communication intervention using these methods is feasible if recommendations to strengthen screening and recruitment are adopted. Importantly, hiring of bilingual research assistants, and engagement with community and consumer advocates is essential. Early involvement of clinical and interpreting staff as key stakeholders is likewise recommended. Conclusion: Results from this feasibility RCT help us better understand and overcome the challenges and misconceptions about including migrant patients in clinical 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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".