Using outcome harvesting: Assessing the efficacy of CBME implementation
Bibliographic record
Abstract
RATIONALE: In 2015, Queen's University embarked on an institution-wide transition to a competency-based medical education (CBME) curriculum for all 29 postgraduate medical education programmes. On 1 July 2017, this goal was accomplished. With this mass transition came the requirement to assess the efficacy of implementation through a programme evaluation process, which included the use of outcome harvesting (Wilson-Grau). Outcome harvesting identified the intended and unintended outcomes of CBME implementation, which helped us understand how the intervention was achieved and how the relationship between behaviours and stakeholders contributed to the successful transition. METHODS: A systematic approach to document analysis was used to categorize the eight identified areas of implementation: governance, scholarship, faculty development, resident leadership, curriculum, assessment, communications, and technology. Documents (N = 443) were organized per project area and then coded thematically. Documents were then categorized for attribution to outcomes using the outcome harvesting approach. Outcomes were validated via interrater reliability and substantiated by stakeholders to verify accuracy of formulation and plausibility of its influence on the outcome. RESULTS: The harvest produced 38 outcomes, either intended or unintended, that can be attributed to CBME implementation at Queen's University. CONCLUSION: Using outcome harvesting to assess the efficacy of CBME implementation produced a robust set of themes and resultant outcomes that can be categorized as requirements for success of implementation of any curricular innovation. Emergent themes included collaboration, community of practice, and stakeholder commitment. More unique observations noted through the harvest process included new policy development, creation of learner ownership, and an increase in the output of scholarly activity involving CBME.
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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 | 0.165 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".