CPD of the future: a partnership between quality improvement and competency‐based education
Bibliographic record
Abstract
CONTEXT: Many of those involved in continuing professional development (CPD) over the past 10 years have engaged in discussions about its goals and activities. Whereas in the past CPD was viewed as an education intervention directed towards the medical expert role, recent research highlights the need to expand the scope of CPD and to promote its more explicit role in improving patient care and health outcomes. Recent developments in quality improvement (QI) and competency-based medical education (CBME), guided by appropriate theories of learning and change, can shed light on how the field might best advance. This paper describes principles of QI and CBME and how they might contribute to CPD, explores theoretical perspectives that inform such an integration and suggests a future model of CPD. DISCUSSION: Continuing professional development seeks to improve patient outcomes by increasing physician knowledge and skills and changing behaviours, whereas QI takes the approach of system and process change. Combining the strengths of a CPD approach with strategies known to be effective from the field of QI has the potential to harmonise the contributions of each, and thereby to lead to better patient outcomes. Similarly, competency-based CPD is envisioned to place health needs and patient outcomes at the centre of a CPD system that will be guided by a set of competencies to enhance the quality of practice and the safety of the health system. CONCLUSIONS: We propose that the future CPD system should adhere to the following principles: it should be grounded in the everyday workplace, integrated into the health care system, oriented to patient outcomes, guided by multiple sources of performance and outcome data, and team-based; it should employ the principles and strategies of QI, and should be taken on as a collective responsibility by physicians, CPD provider organisations, regulators and the health system.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".