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 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 | 0.068 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".