Bibliographic record
Abstract
Problem: Training healthcare professionals in Quality Improvement (QI) has been highlighted as a potential strategy to reduce the prevalence of error and harm in healthcare. As a result, various health professions education programs have integrated QI into the competency frameworks that inform the core curriculum, including those used in the training of medical doctors. However, QI has been integrated, emphasized, and taught to medical trainees (i.e., medical students and residents) in a variety of ways across countries, programs, and stages of training. As contemporary medical education increasingly adapts outcomes-oriented, competency-based models of training, medical trainees may be required to demonstrate competency in QI during their training. Method of Study: This research considered how best to train future physicians in QI during their core medical training. First, methods for examining complex social phenomena were analyzed through a thought experiment exploring the methodological intersections of realist inquiry (RI) with structural equation modelling (SEM). Next, a realist synthesis examined the literature for teaching QI at the undergraduate and postgraduate levels of medical training. This generated an explanatory program theory that highlighted common associations between contexts, mechanisms, and outcomes of QI training in undergraduate and postgraduate medical training. Finally, a collective case study of four postgraduate programs at the University of Calgary examined how residents learned about QI during their training using four data sources. The combinations of RI and SEM were re-visited and operationalized as the program theory informed the specification of structural models using the quantitative data in the case study. This resulted in a novel, realist-informed SEM that statistically modelled elements associated with resident self-assessments of QI knowledge, skills, and attitudes. Conclusions: Explicit training in QI might ensure that all physicians enter practice equipped with the fundamental knowledge and skills to not only recognize areas for improvement, but implement sustainable solutions that improve the quality and safety of care. The conscientious design of QI curricula in the core medical curriculum that considers integrating features commonly associated with successful QI curricula may be beneficial to optimize training in this domain, and ultimately, catalyze the development of QI competencies amongst future physicians.
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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 | 0.046 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 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".