Accreditation Approaches for Professional Education Programs: Toward Best Practice
Bibliographic record
Abstract
Accreditation has a central role in the quality assurance of professional education programs, but research on the effectiveness of different models of accreditation is limited. The purpose of this study was to rapidly appraise the evidence for the effectiveness, impact, and feasibility of different accreditation approaches, in order to inform best practices for the accreditation of professional education programs. The study focused on accreditation for programs that produce practice-ready graduates, including for veterinary programs. The authors searched several databases for articles published from 2000 to 2020, using search terms identified during a scoping phase, and applied a "rapid review" methodology in line with contextual, time, and resource requirements. Relevant articles that were classed as empirical or conceptual were included in the study, while papers appraised as solely commentaries or descriptive were excluded from the evidence base. The full-text review included 32 articles. We identified a clear transition in the literature from input- and process-based models (pre- and early 2000s) to outcomes-based models (in the 2000s and early 2010s). Continuous quality improvement and targeted models (including risk-based and thematic) represent more recent approaches in accreditation practice. However, as noted by other scholars, we identified limited empirical evidence for the relative effectiveness of different accreditation approaches in professional education, although evidence for the more recent accreditation approaches is emerging. In terms of best practice in view of the current lack of definitive evidence for the adoption of any specific model of accreditation, we argue that accrediting authorities adopt a contextual approach to accreditation that includes clearly articulating the purpose and focus of their regulatory activities, and selecting and implementing accreditation methods that are consistent with their underlying principles.
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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 | 0.295 | 0.412 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".