“We are not invited”: Australian focus group results on how to improve ethnic diversity in trials
Bibliographic record
Abstract
OBJECTIVES: Lack of ethnic diversity in trials may contribute to health disparities and to inequity in health outcomes. The primary objective was to investigate the experiences and perspectives of ethnically diverse populations about how to improve ethnic diversity in trials. STUDY DESIGN AND SETTING: Qualitative data were collected via 16 focus groups with participants from 21 ethnically diverse communities in Australia. Data collection took place between August and September 2022 in community-based settings in six capital cities: Sydney, Melbourne, Perth, Adelaide, Brisbane, and Darwin, and one rural town: Bordertown (South Australia). RESULTS: One hundred and fifty-eight purposively sampled adults (aged 18-85, 49% women) participated in groups speaking Tamil, Greek, Punjabi, Italian, Mandarin, Cantonese, Karin, Vietnamese, Nepalese, and Arabic; or English-language groups (comprising Fijian, Filipino, African, and two multicultural groups). Only 10 participants had previously taken part in medical research including three in trials. There was support for medical research, including trials; however, most participants had never been invited to participate. To increase ethnic diversity in trial populations, participants recommended recruitment via partnering with communities, translating trial materials and making them culturally accessible using audiovisual ways, promoting retention by minimizing participant burden, establishing trust and rapport between participants and researchers, and sharing individual results. Participants were reluctant to join studies on taboo topics in their communities (eg, sexual health) or in which physical specimens (eg, blood) were needed. Participants said these barriers could be mitigated by communicating about the topic in more culturally cognizant and safe ways, explaining how data would be securely stored, and reinforcing the benefit of medical research to humanity. CONCLUSION: Participants recognized the principal benefits of trials and other medical research, were prepared to take part, and offered suggestions on recruitment, consent, data collection mechanisms, and retention to enable this to occur. Researchers should consider these community insights when designing and conducting trials; and government, regulators, funders, and publishers should allow for greater innovation and flexibility in their processes to enable ethnic diversity in trials to improve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.306 | 0.885 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.013 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".