Development and implementation of a postgraduate medical education-wide initiative in quality improvement and patient safety
Bibliographic record
Abstract
BACKGROUND: Quality improvement and patient safety (QIPS) have been assigned a higher profile in CanMEDS 2015, CanMEDS-Family Medicine 2017 and new accreditation standards, prompting an initiative at Dalhousie University to create a vision for integrating QIPS into postgraduate medical education. OBJECTIVE: The purpose of this study is to describe the implementation of a QIPS strategy across residency education at Dalhousie University. METHODS: A QIPS task force was formed, and a literature review and needs assessment survey were completed. A needs assessment survey was distributed to all Dalhousie residency programme directors. 12 programme directors were interviewed individually to collect additional feedback. The results were used to develop a 'road map' of recommendations with a graduated timeline. RESULTS: A task force report was released in February 2018. 46 recommendations were developed with a timeframe and responsible party identified for each. Implementation of the QIPS strategy is underway, and evaluation and challenges faced will be described. CONCLUSIONS: We have developed a multiyear strategy that is available to provide guidance and support to all programmes in QIPS. The development and implementation of this QIPS framework may serve as a template for other institutions who seek to integrate these competencies into residency training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".