Understanding of evaluation capacity building in practice: a case study of a national medical education organization
Bibliographic record
Abstract
INTRODUCTION: Evaluation capacity building (ECB) is a topic of great interest to many organizations as they face increasing demands for accountability and evidence-based practices. ECB is about building the knowledge, skills, and attitudes of organizational members, the sustainability of rigorous evaluative practices, and providing the resources and motivations to engage in ongoing evaluative work. There exists a solid foundation of theoretical research on ECB, however, understanding what ECB looks like in practice is relatively thin. Our purpose was to investigate what ECB looks like firsthand within a national medical educational organization. METHODS: The context for this study was the Acute Critical Events Simulation (ACES) organization in Canada, which has successfully evolved into a national educational program, driven by physicians. We conducted an exploratory qualitative study to better understand and describe ECB in practice. In doing so, interviews were conducted with program leaders and instructors so as to gain a richer understanding of evaluative processes and practices. RESULTS: A total of 21 individuals participated in the semistructured interviews. Themes from our qualitative data analysis included the following: evaluation knowledge, skills, and attitudes, use of evaluation findings, shared evaluation beliefs and commitment, evaluation frameworks and processes, and resources dedicated to evaluation. CONCLUSION: The national ACES organization was a useful case study to explore ECB in practice. The ECB literature provided a solid foundation to understand the purpose and nuances of ECB. This study added to the paucity of studies focused on examining ECB in practice. The most important lesson learned was that the organization must have leadership who are intrinsically motivated to employ and use evaluation data to drive ongoing improvements within the organization. Leaders who are intrinsically motivated will employ risk taking when evaluation practices and processes may be somewhat unfamiliar. Creating and maintaining a culture of data use and ongoing inquiry have enabled national ACES to achieve a sustainable evaluation practice.
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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 | 0.030 | 0.378 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".