“We Don’t Get Drugs Targeted for Us:” Applying the Integrated Behavioral Model to Understand Why Black Women Chose to Participate in a Breast Cancer Clinical Trial
Bibliographic record
Abstract
= 14) who had participated in a breast cancer clinical trial. This study aimed to better understand what may motivate Black women to engage in medical research and decide to participate in medical research. Findings revealed that Black women's altruistic desires to serve others and their communities are greatly influenced by the need to leave a "legacy" of better treatment for other Black women. The participants mostly learned about clinical trials through communicating with friends, family, or other breast cancer patients and survivors, rather than from their physicians. Many were influenced to participate by other Black breast cancer patients they knew, suggesting that social norms messaging may help alert other Black women about the continuing disparity in clinical trial participation. Finally, the participants in this study demonstrated high levels of involvement not only in seeking out clinical trials, but also in engaging in informed and shared decision-making with their providers about participating in the trials. The findings from this work illuminate important reasons Black women chose to participate in breast cancer clinical trials. Additionally, we offer robust and valuable theoretical and practical implications for researchers, so they can work toward successfully increasing Black women's participation in clinical trials.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".