Impact of a competency based curriculum on quality improvement among internal medicine residents
Bibliographic record
Abstract
BACKGROUND: Teaching quality improvement (QI) principles during residency is an important component of promoting patient safety and improving quality of care. The literature on QI curricula for internal medicine residents is limited. We sought to evaluate the impact of a competency based curriculum on QI among internal medicine residents. METHODS: This was a prospective, cohort study over four years (2007-2011) using pre-post curriculum comparison design in an internal medicine residency program in Canada. Overall 175 post-graduate year one internal medicine residents participated. A two-phase, competency based curriculum on QI was developed with didactic workshops and longitudinal, team-based QI projects. The main outcome measures included self-assessment, objective assessment using the Quality Improvement Knowledge Assessment Tool (QIKAT) scores to assess QI knowledge, and performance-based assessment via presentation of longitudinal QI projects. RESULTS: Overall 175 residents participated, with a response rate of 160/175 (91%) post-curriculum and 114/175 (65%) after conducting their longitudinal QI project. Residents' self-reported confidence in making changes to improve health increased and was sustained at twelve months post-curriculum. Self-assessment scores of QI skills improved significantly from pre-curriculum (53.4 to 69.2 percent post-curriculum [p-value 0.002]) and scores were sustained at twelve months after conducting their longitudinal QI projects (53.4 to 72.2 percent [p-value 0.005]). Objective scores using the QIKAT increased post-curriculum from 8.3 to 10.1 out of 15 (p-value for difference <0.001) and this change was sustained at twelve months post-project with average individual scores of 10.7 out of 15 (p-value for difference from pre-curriculum <0.001). Performance-based assessment occurred via presentation of all projects at the annual QI Project Podium Presentation Day. CONCLUSION: The competency based curriculum on QI improved residents' QI knowledge and skills during residency training. Importantly, residents perceived that their QI knowledge improved after the curriculum and this also correlated to improved QIKAT scores. Experiential QI project work appeared to contribute to sustaining QI knowledge at twelve months.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".