A priority educational program at Princess Margaret Cancer Centre.
Bibliographic record
Abstract
189 Background: The clinical research environment is consistently evolving with new methodology, complexity and regulatory stringency. With this evolution, the need for a well-educated clinical research team is critical. There are approximately 300 clinical research staff at Princess Margaret, 100 Principal investigators. The Cancer Clinical Research Unit (CCRU) is a support department within the Princess Margaret Cancer Program and provides two dedicated staff to implement and facilitate clinical research education across the program. Methods: CCRU Education currently offers 50 unique education sessions with an average of 45 sessions per quarter. Starting in 2010, 9275 attendees have attended 892 CCRU in-class sessions. Since 2015, 224 sessions have been attended by 25 sites through teleconference. The CCRU has implemented an “Orientation Pathway” for new clinical research staff to provide clear guidance on mandatory research training activities, as well as role and task-specific training activities. In 2016, CCRU has included more workshop-style courses, providing scenario-based learning models. The CCRU uses a blend of online, in-class, and case-based learning sessions to promote critical thinking and stimulate vibrant group discussion. Interactive sessions and evaluation ensure learning needs are met. Results: Since implementing the interactive sessions, and the Orientation Pathways, the CCRU Quality Assurance department has seen a reduction of insufficient documentation findings. To measure this, the total number of Quality Assurance Review (QAR) findings on insufficient and/or incomplete documentation of the consent process was averaged and compared in 2014 and 2015 and a 25% decrease was noted. In addition, staff feedback is regularly collected through an online methods and is reviewed by the CCRU Quality-Education committee for training quality improvement. Conclusions: Positive staff feedback and a reduction of QAR findings has encouraged CCRU Education to continue creating workshop-based training sessions that are interactive, timely and effective. Efforts will continue to include CCRU-QA findings in the development of new course content to ensure staff are well trained and overall quality continues to improve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.361 | 0.066 |
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".