Closing the gap: a transatlantic collaboration to foster quality improvement training in graduate entry medical students using applications of QI methodologies to medical education
Bibliographic record
Abstract
The alarming prevalence of medical error and adverse events in the health system raises a call to action to ensure that doctors in training receive adequate training in quality improvement (QI). Training medical students in QI remains a challenge given time constraints, lack of clinical exposure, and already saturated curricula. In some instances, QI training may be delivered during clerkship through didactic, and in some instances, and experiential learning. Preclinical years of medical school remain focused on introducing students to scientific and clinical concepts, rarely do they learn about QI. The Program for Innovation in Scholarship and Medicine (PRISM) is a programme that introduces first-year medical students to the fundamentals of QI using their experience as a medical student as the context. PRISM is a condensed QI curriculum that is delivered through an international partnership, based on a previously piloted programme at a Canadian medical school. Following an introductory workshop, medical students work in teams to develop QI proposals (project charters) which detail how QI principles and tools can generate small-scale improvements within their educational programme. Project charters are assessed by a team of faculty and upper year students, who have previously participated. On completion of the programme, students demonstrated increased knowledge, skills, and attitudes towards QI. Programme participants were satisfied with the structure and expectations of PRISM and expressed a newfound interest in QI. Nearly all participants would recommend PRISM to another medical student. In conclusion, PRISM serves as a resourceful, efficient educational approach for preclerkship students that provides an introduction to the concepts of QI in order for early trainees to build on baseline knowledge and skills throughout their training.
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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 | 0.039 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".