Recruiting Postmenopausal Women into Randomized Controlled Trials: A Patient Perspective
Bibliographic record
Abstract
Purpose: To identify barriers to, and motivations for, recruitment and retention in osteoporosis related clinical trials among postmenopausal women. Methods: We explored the self reported reasons for and against participation in clinical trials among women who expressed an interest in participating in the Nitrates and Bone Turnover (NABT) study: an ongoing randomized controlled trial based at an urban tertiary care centre (Women’s College Hospital, University of Toronto). The study was designed to compare the effects of different doses and formulations of nitrates on markers of bone turnover among postmenopausal women not diagnosed and/or receiving treatment for osteoporosis. We administered a standardized interviewer questionnaire to 53 women to determine their reasons for participation in the NABT trial. To determine reasons for non-participation, we administered a questionnaire to 9 women and reviewed data collected at the time of initial assessment in 56 women who were not interested in participating in the trial. We conducted qualitative analyses using thematic coding of these responses. Results: The most common reasons for participation were: altruism (26.4%) and potential personal benefits (22.6%). The two most common reasons for non-participation included fear associated with taking medication (23.1%) and lack of time (16.9%). Conclusions: Postmenopausal women participate in clinical trials to help others and potentially themselves. Barriers to participation in trials may include the intervention being evaluated and time required to participate in the trial. Researchers should consider these motivations and barriers when recruiting postmenopausal women for RCTs.
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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 | 0.206 | 0.361 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".