Perspectives on inclusion, diversity, equity, and access in clinical trials: findings from a 6-continent survey
Bibliographic record
Abstract
OBJECTIVE: Clinical trials require the inclusion of all relevant demographic groups, including under-represented populations, to ensure accurate and representative findings. The aim of the study was to assess the status quo of inclusion, diversity, equity, and access in clinical trials across various countries. METHODS: An 18-item online survey was developed and administered to 5 people. The questionnaire was distributed to delegates from gynecologic research groups in the Gynecologic Cancer Intergroup Network worldwide. All the analyses are purely descriptive. RESULTS: A total of 73 participants (86.3% physicians and 47.9% female) from 33 countries participated in the survey; 91.8% deemed the inclusion of under-represented groups in clinical trials important, and 91.2% supported increasing representation in phase III trials. Most participants believed that language barriers (68.7%) and restricted eligibility criteria (56.7%) were the main reasons for under-representation. Language barriers are seen as more significant in Africa and Europe than in Asia (83.3% and 75.0% vs 58.6%, respectively). Limited patient knowledge about clinical trials (73.1%) was also cited as a key issue. Only 20.5% reported having a minimal data set to document demographic groups. The most helpful measure was the provision of trial information in various languages (69.7%). Overall, women were more supportive of all the suggested improvement measures than were men. CONCLUSIONS: There is a need for better strategies to improve diversity in clinical trials, focusing on overcoming language barriers and eligibility constraints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".