Data and Lifelong Learning Protocol: Understanding Cultural Barriers and Facilitators to Using Clinical Performance Data to Support Continuing Professional Development
Bibliographic record
Abstract
Continuing professional development (CPD) can support delivery of high-quality care, but may not be optimized until we can understand cultural barriers and facilitators, especially as innovations emerge. Lifelong learning (LLL), linked with quality improvement, competence, and professionalism, is a core competency in medical education. The purpose of this study is to examine cultural factors (individual, organizational, and systemic) that influence CPD and specifically the use of clinical data to inform LLL and CPD activities. This mixed-method study will examine the perceptions of two learner groups (psychiatrists and general surgeons) in three phases: (1) a survey to understand the relationship between data-informed learning and orientation to LLL; (2) semistructured interviews using purposive and maximum variation sampling techniques to identify individual-, organizational-, and system-level barriers and facilitators to engaging in data-informed LLL to support practice change; and (3) a document analysis of legislation, policies, and procedures related to the access and the use of clinical data for performance improvement in CPD. We obtained research ethics approval from the University Health Network in Toronto, Ontario, Canada. By exploring two distinct learner groups, we will identify contextual features that may inform what educators should consider when conceptualizing and designing CPD activities and what initial actions need to be taken before CPD activities can be optimized. This study will lead to the development of a framework reflective of barriers and facilitators that can be implemented when planning to use data in CPD activities to support data adoption for LLL.
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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 | 0.134 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.112 | 0.031 |
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".