How can systems engineering inform the methods of programme evaluation in health professions education?
Bibliographic record
Abstract
CONTEXT: We evaluate programmes in health professions education (HPE) to determine their effectiveness and value. Programme evaluation has evolved from use of reductionist frameworks to those addressing the complex interactions between programme factors. Researchers in HPE have recently suggested a 'holistic programme evaluation' aiming to better describe and understand the implications of 'emergent processes and outcomes'. FRAMEWORK: We propose a programme evaluation framework informed by principles and tools from systems engineering. Systems engineers conceptualise complexity and emergent elements in unique ways that may complement and extend contemporary programme evaluations in HPE. We demonstrate how the abstract decomposition space (ADS), an engineering knowledge elicitation tool, provides the foundation for a systems engineering informed programme evaluation designed to capture both planned and emergent programme elements. METHODS: We translate the ADS tool to use education-oriented language, and describe how evaluators can use it to create a programme-specific ADS through iterative refinement. We provide a conceptualisation of emergent elements and an equation that evaluators can use to identify the emergent elements in their programme. Using our framework, evaluators can analyse programmes not as isolated units with planned processes and planned outcomes, but as unfolding, complex interactive systems that will exhibit emergent processes and emergent outcomes. Subsequent analysis of these emergent elements will inform the evaluator as they seek to optimise and improve the programme. CONCLUSION: Our proposed systems engineering informed programme evaluation framework provides principles and tools for analysing the implications of planned and emergent elements, as well as their potential interactions. We acknowledge that our framework is preliminary and will require application and constant refinement. We suggest that our framework will also advance our understanding of the construct of 'emergence' in HPE research.
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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 | 0.258 | 0.355 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier 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".