To prove or improve? Examining how paradoxical tensions shape evaluation practices in accreditation contexts
Bibliographic record
Abstract
INTRODUCTION: Although programme evaluation is increasingly routinised across the academic health sciences, there is scant research on the factors that shape the scope and quality of evaluation work in health professions education. Our research addresses this gap, by studying how the context in which evaluation is practised influences the type of evaluation that can be conducted. Focusing on the context of accreditation, we critically examine the types of paradoxical tensions that surface as evaluation-leads consider evaluation ideals or best practices in relation to contextual demands associated with accreditation seeking. METHODS: Our methods were qualitative and situated within a critical realist paradigm. Study participants were 29 individuals with roles requiring responsibility and oversight on evaluation work. They worked across 4 regions, within 26 academic health science institutions. Data were collected using semi-structured interviews and analysed using framework and matrix analyses. RESULTS: We identified three overarching themes: (i) absence of collective coherence about evaluation practice, (ii) disempowerment of expertise and (iii) tensions as routine practice. Examples of these latter tensions in evaluation work included (i) resourcing accreditation versus resourcing robust evaluation strategy (performing paradox), (ii) evaluation designs to secure accreditation versus design to spur renewal and transformation (performing-learning paradox) and (iii) public dissemination of evaluation findings versus restricted or selective access (publicising paradox). Sub-themes and illustrative data are presented. DISCUSSION: Our study demonstrates how the high-stakes context of accreditation seeking surfaces tensions that can risk the quality and credibility of evaluation practices. To mitigate these risks, those who commission or execute evaluation work must be able to identify and reconcile these tensions. We propose strategies that may help optimise the quality of evaluation work alongside accreditation-seeking efforts. Critically, our research highlights the limitations of continually positioning evaluation purely as a method versus as a socio-technical practice that is highly vulnerable to contextual influences.
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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 | 0.167 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.053 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".