Barriers to Participation in Clinical Trials: a Physician Survey
Bibliographic record
Abstract
Background: Clinical trials are vital for evidence-based cancer care. Oncologist engagement in clinical trials has an effect on patient recruitment, which in turn can affect trial success. Identifying barriers to clinical trial participation might enable interventions that could help to increase physician participation. Methods: To assess factors affecting physician engagement in oncology trials, a national survey was conducted using the online SurveyMonkey tool (SurveyMonkey, San Mateo, CA, U.S.A.; http://www.surveymonkey.com). Physicians associated with the Canadian Cancer Clinical Trials Network and the Canadian Cancer Trials Group were asked about their specialty, years of experience, barriers to participation, and motivating interventions, which included an open-ended question inviting survey takers to suggest interventions. Results: The survey collected 207 anonymous responses. Respondents were predominantly medical oncologists (46.4%), followed by radiation oncologists (24.6%). Almost 70% of the respondents had more than 10 years of experience. Significant time constraints included extra paperwork (77%), patient education (54%), and extended follow-up or clinic visits (53%). Timing of events within trials was also a barrier to participation (55%). Most respondents favoured clinical work credits (72%), academic credits (67%), a clinical trial alert system (75%), a regular meeting to review trial protocols (65%), and a screening log to aid in patient accrual (67%) as motivational strategies. Suggested interventions included increased support staff, streamlined regulatory burden, and provision of greater funding for trials and easier access to ancillary services. Conclusions: The present study confirms that Canadian oncologists are willing to participate in clinical research, but face multiple barriers to trial participation. Those barriers could be mitigated by the implementation of several interventions identified in the study.
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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 | 0.064 | 0.441 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".