Implementing a quality improvement curriculum for medical oncology residents: A pilot study at the Ottawa Hospital Cancer Centre.
Bibliographic record
Abstract
10540 Background: With increasing cancer care costs and demands in Canada, quality improvement (QI) efforts are urgently needed. Yet no formal QI education exists in the Canadian Medical Oncology setting. We created an Oncology-specific QI curriculum and sought to assess its feasibility and efficacy among Medical Oncology residents. Methods: In this prospective, pre-experimental pilot study using a pre-post curriculum design, Medical Oncology residents at The Ottawa Hospital Cancer Centre participated in a new QI curriculum. It consisted of four 2-hour sessions encompassing a combination of didactic and interactive learning. The primary measures were self-assessment of confidence in QI skills with the Self-Assessment Program (SAP) and objective assessment of QI knowledge with the revised QIKAT (QIKAT-R). The SAP and QIKAT-R were completed at baseline and post-curriculum. The primary outcome was feasibility of the educational approach. Results: Five Medical Oncology participated, while four (80%) completed the assessments at both timepoints. Self-assessment in the skills needed to execute a process improvement project improved with participation in the curriculum. Mean SAP scores improved from 19.6 pre-curriculum to 33.5 post-curriculum. SAP scores improved for each of the 10 quality improvement skills evaluated. Objective assessment using the QIKAT-R also improved post-curriculum, with a mean score of 17 pre-curriculum and 24 post-curriculum. Mean scores of each domain of “Aim, Measure, and Change” evaluated by the QIKAT-R improved. Conclusions: Self-assessed confidence and objective knowledge in QI concepts in Medical Oncology residents improved after participation in this Oncology-specific QI curriculum. Feasibility of this approach was demonstrated, and therefore a larger scale study will be implemented in the future.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".