Creating an educational quality improvement program for radiation oncology residents in McGill University.
Bibliographic record
Abstract
118 Background: Quality insurance (QI) is a pillar of good clinical governance and is at the centre of modern health care. The Royal College of Physicians and Surgeons of Canada CanMeds 2015 have now mandated that QI should be taught and the competencies assessed in all post-graduate residency programs. To our knowledge, this is the first attempt to create a post-graduate QI curriculum amongst radiation oncology trainees. We aim to describe the feasibility of introducing these professional skills which should be integral to every training program. Methods: A QI team has been created within the department of Radiation Oncology at McGill University consisting of a clinical fellow and 3 staff physicians. QI teaching will take place in a longitudinal manner with the mandatory curriculum divided into foundation, intermediate and advanced competencies depending on years of seniority. Teaching is delivered by a combination of two academic half days, consisting of didactic lectures and practical workshops, and self-directed online modules. Each resident during the intermediate years (PGY2-4) will complete a QI project in 9 months under the supervision of an attending physician. The resident will become well versed with QI tools and techniques by presenting their project at specific 3-monthly time points to their supervisor and QI team. In June we will host a QI day where a QI scholar will be invited to teach, each resident will present their project and merit prizes will be awarded. Formal mandatory assessments will take place with a combination of self-assessment, QI- knowledge based assessments (QI-KATs) and balanced score cards. Results: The curriculum has been developed with input from McGill University curriculum and assessment experts. This is a pilot program for the academic 2016/17 year. We are currently meeting our pre-defined milestones. The program will be formally evaluated and adapted to ensure sustainability. Conclusions: The QI skills gained will enable the individual to maintain the highest standards throughout their subsequent careers. A robust, interactive, sustainable curriculum will ensure that this is delivered effectively within radiation oncology and act as a model for all residency programmes.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".