Technology-enabled assessment of health professions education: Consensus statement and recommendations from the Ottawa 2010 conference
Bibliographic record
Abstract
The uptake of information and communication technologies (ICTs) in health professions education can have far-reaching consequences on assessment. The medical education community still needs to develop a deeper understanding of how technology can underpin and extend assessment practices. This article was developed by the 2010 Ottawa Conference Consensus Group on technology-enabled assessment to guide practitioners and researchers working in this area. This article highlights the changing nature of ICTs in assessment, the importance of aligning technology-enabled assessment with local context and needs, the need for better evidence to support use of technologies in health profession education assessment, and a number of challenges, particularly validity threats, that need to be addressed while incorporating technology in assessment. Our recommendations are intended for all practitioners across health professional education. Recommendations include adhering to principles of good assessment, the need for developing coherent institutional policy, using technologies to broaden the competencies to be assessed, linking patient-outcome data to assessment of practitioner performance, and capitalizing on technologies for the management of the entire life-cycle of assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.204 | 0.173 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.016 | 0.018 |
| Research integrity | 0.018 | 0.029 |
| 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".