Abstract 3590: Quantification of Resources Needed for Successful Recruitment for Multiracial Stroke Randomized Controlled Trials
Bibliographic record
Abstract
Background: Many stroke trials fail to meet recruitment goals or need additional time to accrue sufficient subjects to support hypothesis testing. Many investigators also report difficulties recruiting diverse samples. One cause may be a lack of understanding of the complexity of the process and insufficient resources devoted to recruitment efforts. We s quantified and analyzed the number of interactions needed to present study materials and obtain a final response to an invitation to participate in a stroke intervention trial seeking a diverse sample. Methods: Patients with acute ischemic stroke or TIA admitted to five hospitals in the District of Columbia were screened for enrollment in PROTECT-DC (Prevention of Recurrent Throboembolic Events District of Columbia). All participants were approached during the acute phase of stroke treatment. No recruitment occurred in the ED. Recruitment staff were trained for cultural competency, culturally tailored recruitment materials and scripts were provided. Detailed logs of each encounter were made. Data included number of face-to-face contacts and telephone conversations with potential participants and family members. Results: from June 2009 to June 2011, 1915 patients were admitted with stroke, 619 (32%) met inclusion criteria, mean age 65, 52 % female and 66 % African American. Of, these 286 (46%) patients were discharged before being approached. Of the total eligible 333 (54%) were approached ; 130 (39%) consented, 109 (33%) refused the remaining 94 (28 %) made no decision.. The mean number of contacts before obtaining a decision was 2.81 ± 1.15 (range 1-6). There were no significant differences in consent rates by gender (p=0.44) or race (p=0.64), however blacks required significantly more contacts (p<0.04) than whites before consenting or refusing participation. Review of recruitment log data suggests that substantial time and effort is devoted to individuals who do not wish to volunteer, but who are unwilling to decline participation. Conclusion: Approximately recruitment visits were required to successfully recruit subjects for this non ED hospital based stroke prevention trial. On average recruiters spent 5 hours in direct recruitment activities per consented subject. Substantial effort is associated with “soft refusals” in patients who do not wish to volunteer. Successful recruitment requires adequate resources and appropriate staff training and support. It is important to recognize these resource needs when planning a stroke intervention trial.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".