Understanding the Perspectives of Potential Minority Participants on Clinical Trial Enrollment
Bibliographic record
Abstract
Research has shown that there continues to be insufficient recruitment of minorities in clinical trials. By eliminating this group from research, not only does it impact the success of clinical trials by making it more difficult to achieve recruitment targets, but it also leads to an inability to identify appropriate treatments and interventions for all individuals, especially as racial/ethnic factors can play a role in the efficacy and safety of a treatment and intervention. The purpose of the study was to understand the perspectives of minority healthcare students/professionals on clinical trial enrollment. Focusing on this population would shed light on how minority individuals feel about clinical trial enrollment and whether healthcare professionals can be used as an avenue to share clinical research opportunities with the general minority population. The study was a general qualitative study utilizing a semi-structured interview guide to collect the data. The interview guide was designed to explore the thought process of minorities when it comes to enrollment and understand what would influence their decision. The sample population consisted of 20 individuals who were recruited in an educational setting with the participants being graduate healthcare students either working at the same time as healthcare professionals or who would work in the near future as healthcare professionals. The data analysis strategy was data driven, also known as inductive analysis, and was done through coding. Eight themes that influence clinical trial participation by minorities emerged from the interviews which were benefits, negative aspects, need for information, support, diversity, religion and faith values, family and friends, and wanting better collective health. The Ottawa Decision Support Framework was used as a lens to interpret the themes and found that it explained some of the themes. The results of the study show that while minority healthcare students/professionals find clinical trials innovative and important, they do not understand the specifics about them and not all are open to participating. Therefore, there is a need to address potential minority participants’ decisional needs and in general, educate, or there will continue to be an inequality in treatments and interventions.
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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 | 0.108 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".