Trainee Evaluations of Preparedness for Clinical Trials in Medical Oncology—A National Questionnaire
Bibliographic record
Abstract
Background: A standardized approach to the education of clinical trial investigators across Canadian medical oncology (MO) subspecialty training does not exist. With training programs transitioning to competency-based medical education (CBME), studies assessing education practices and competence are paramount to enhancing trainee education. This study aimed to determine whether current education practices in MO subspecialty training programs in Canada prepare trainees for participating in clinical trials as an investigator. Methods: From November 2021 to February 2022 a national, bilingual, online questionnaire to understand trainee experiences with self-perceived competence, preparedness, and willingness to participate in clinical trials as investigators was conducted. MO trainees, fellows, and new-to-practice physicians who completed an MO subspecialty training program in Canada were included. Results: A total of 41 responses were received (response rate: 15%). Formal training in how to participate in clinical trials as an investigator was reported by 73% of respondents. At the end of training, 65% of respondents rated competence in clinical trials as fair/poor and 74% rated preparedness in conducting clinical trials as fair/poor. Correlation analysis determined that in-clinic teaching in clinical trials trended toward improved self-evaluations of competence and preparedness (p > 0.05). Conclusion: This is the first study in Canada to assess competencies in any residency training program since the establishment of CBME. Training in conducting clinical trials is highly variable across MO programs in Canada, with most trainees finding current practices not translating into self-perceived competence and preparedness. Further assessment into how to produce competent clinical trial investigators is warranted.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".