Ottawa 2020 consensus statements for programmatic assessment 2: Implementation and practice
Bibliographic record
Abstract
Programmatic assessment is a longitudinal, developmental approach that fosters and harnesses the learning function of assessment. Yet the implementation, a critical step to translate theory into practice, can be challenging. As part of the Ottawa 2020 consensus statement on programmatic assessment, we sought to provide descriptions of the implementation of the 12 principles of programmatic assessment and to gain insight into enablers and barriers across different institutions and contexts. After the 2020 Ottawa conference, we surveyed 15 Health Profession Education programmes from six different countries about the implementation of the 12 principles of programmatic assessment. Survey responses were analysed using a deductive thematic analysis. A wide range of implementations were reported although the principles remained, for the most part, faithful to the original enunciation and rationale. Enablers included strong leadership support, ongoing faculty development, providing students with clear expectations about assessment, simultaneous curriculum renewal and organisational commitment to change. Most barriers were related to the need for a paradigm shift in the culture of assessment. Descriptions of implementations in relation to the theoretical principles, across multiple educational contexts, coupled with explanations of enablers and barriers, provided new insights and a clearer understanding of the strategic and operational considerations in the implementation of programmatic assessment. Future research is needed to further explore how contextual and cultural factors affect implementation.
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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 | 0.302 | 0.434 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.016 | 0.014 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".