Assessment of barriers to patient participation in clinical trials among oncology outpatients at a community teaching hospital in Toronto
Bibliographic record
Abstract
e17547 Background: This study investigated barriers to patient participation in clinical trials (CTs). In particular, the effect of educational attainment on the understanding of, interest about, and past participation in CTs among cancer patients at a Toronto community teaching hospital was assessed. Methods: A survey was administered to cancer outpatients, containing Yes/No answers, and likert scales. Responses were analyzed quantitatively (SPSS). Results: 101 questionnaires were completed out of 112 administered (90% response rate). 76% of patients self-reported as knowledgeable about clinical trials. Patients education level correlated to their perceived knowledge of clinical trials: 37.5% of patients with a less than grade 9, 59.4% with a high school, and 90.2% with a university background identified themselves as knowing what CTs are. All patients scored poorly on details and held common misconceptions of CTs (e.g. knowing little about placebos and experimental drug testing). Overall, 27% of patients had been previously enrolled in CTs. Of the 34% of patients who had been previously approached, 79% had agreed to participate. The majority were willing to participate or unsure of participating in CTs. A minority stated they will never participate in a trial. Patients also identified concern about lack of hospital resources about CTs and a desire to have a doctor present when learning about CTs. Patients with less education identified a greater desire for a doctor's presence than those with more education. Conclusions: This study revealed lack of staff recruitment, low patient awareness, and lack of availability of trial facilities as potential barriers to participation. Targeted information to patients with different education levels may be appropriate, given variable knowledge about CTs. Education and counseling regarding placebos and experimental drugs should be targeted towards all patients to reduce barriers to patient participation, diminish myths, and increase understanding and interest. In response, this institution will design a brochure for all patients describing clinical trials, providing a glossary of terms, and offering a list of key resources in order to improve awareness and trial recruitment. No significant financial relationships to disclose.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".