Rivalries for attention: insights from a realist evaluation of a postgraduate competency-based medical education implementation in Canada
Bibliographic record
Abstract
BACKGROUND: Implementing competency-based medical education (CBME) in post-graduate medical education (PGME) is a complex process that requires multiple systemic changes in a complex system that is simultaneously engaged in multiple initiatives. These initiatives often compete for attention during the implementation of CBME and produce unintended and unanticipated consequences. Understanding the impact of this context is necessary for evaluating the effectiveness of CBME. The purpose of the study was to identify factors, such as contexts and processes, that contribute to the implementation of CBME. METHODS: We conducted a realist evaluation using data collected from 15 programs through focus groups with residents (2 groups, n = 16) and faculty (one group, n = 8), and semi-structured interviews with program directors (n = 18), and program administrators (n = 12) from 2018 to 2021. Data were analyzed using a template analysis based on a coding framework that was developed from a sample of transcripts, the context-mechanism-outcomes framework for realist evaluations, and the core components of CBME. RESULTS: The findings demonstrate that simultaneous initiatives in the academic health sciences system creates a key context for CBME implementation - rivalries for attention - and specifically, the introduction of curricular management systems (CMS) concurrent to, but separate from, the implementation of CBME. This context influenced participants' participation, communication, and adaptation during CBME implementation, which led to change fatigue and unmet expectations for the collection and use of assessment data. CONCLUSIONS: Rival initiatives, such as the concurrent implementation of a new CMS, can have an impact on how programs implement CBME and greatly affect the outcomes of CBME. Mitigating the effects of rivals for attention with flexibility, clear communication, and training can facilitate effective implementation of CBME.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".