Barriers and facilitators for clinical trial participation among diverse Asian patients with breast cancer.
Bibliographic record
Abstract
108 Background: Recruitment rates for breast cancer trials are low for racial/ethnic minorities. Little is known about factors influencing trial recruitment in Asian patients. Our aim is to examine the barriers and facilitators for participation in trials among multi-ethnic Asian women with breast cancer. Methods: We recruited a convenience sample from consecutive women seen at a National Cancer Centre. Two experienced bilingual (English and Chinese) moderators conducted focus groups to theme saturation. The question guide incorporated open-ended questions soliciting opinions about trial participation and knowledge. Women were first asked if they were willing, unwilling, or still open to participate in future trials. Sessions were audiotaped and transcribed. Transcripts were independently coded for emergent themes. Results: Sixteen of 103 women approached participated in five focus groups. Chinese, Malay and Indian participants aged 29 to 69 represented different cancer stages. Five had no prior knowledge of trials. We identified three major areas consisting of 24 minor themes for barriers and facilitators. Major themes fell into: 1) individual- or patient-related, 2) trial-related and and 3) sociocultural factors. When analysis was stratified by willingness to join trials, we found that women willing to join trials expressed themes representing facilitators (better test therapy, cost-effective profile, or trust in doctors and local systems). Women unwilling to participate expressed themes associated with barriers, while women still open to participation expressed themes representing both facilitators and barriers. Malay women were more likely to express themes related to ‘fatalism’ as a barrier. Conclusions: We found that facilitators and barriers to trial participation among Asian women were similar to those previously reported in Western women. Knowledge of trials is limited among women receiving breast cancer treatment. Unique sociocultural factors suggest that approaches customised to local and community beliefs are needed to improve trial participation in minority groups.
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".