Non-participation in clinical research: Barriers, motivators, and recruitment strategies in an ovarian cancer study
Bibliographic record
Abstract
Clinical studies have made significant contributions to disease prevention, early diagnosis, and new therapies for many diseases, including cancer.However, recruitment of participants is a major challenge in clinical studies on cancer; older adults, minorities, rural residents, and individuals of lower socio-economic status are traditionally difficult to enroll, despite these would like to express my sincere gratitude to my primary supervisor, Dr. Olga Basso, who is abundantly helpful and offers invaluable assistance, support and guidance throughout the duration of my study.I appreciate her vast knowledge and skill in many areas, her assistance in academic writing (i.e., scholarship applications and manuscripts), as well as academic and emotional assistance through the rough road to finish this thesis.Her door is always open to me, and my learning is always her priority.Moreover, I would like to thank Dr. Lucy Gilbert, my cosupervisor, for giving me the opportunity to conduct this study in the context of the large ongoing Diagnosing Ovarian cancer Early (DOvE) Study and providing me financial support, as well as assistance at all levels of the research project.Dr. Basso and Dr. Gilbert are my best role models as scientists, mentors and teachers.I also would like to thank the member of my supervisory committee, Dr. Antonio Ciampi, for his time and statistical advice on my project.I also wish to thank Dr. Jay Kaufman for his epidemiologic and statistical advice regarding my study.Dr. Rebecca Fuhrer has always helped me in the most difficult times during my study
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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 | 0.166 | 0.267 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".