Research in assessment: Consensus statement and recommendations from the Ottawa 2010 Conference
Bibliographic record
Abstract
Medical education research in general is a young scientific discipline which is still finding its own position in the scientific range. It is rooted in both the biomedical sciences and the social sciences, each with their own scientific language. A more unique feature of medical education (and assessment) research is that it has to be both locally and internationally relevant. This is not always easy and sometimes leads to purely ideographic descriptions of an assessment procedure with insufficient general lessons or generalised scientific knowledge being generated or vice versa. For medical educational research, a plethora of methodologies is available to cater to many different research questions. This article contains consensus positions and suggestions on various elements of medical education (assessment) research. Overarching is the position that without a good theoretical underpinning and good knowledge of the existing literature, good research and sound conclusions are impossible to produce, and that there is no inherently superior methodology, but that the best methodology is the one most suited to answer the research question unambiguously. Although the positions should not be perceived as dogmas, they should be taken as very serious recommendations. Topics covered are: types of research, theoretical frameworks, designs and methodologies, instrument properties or psychometrics, costs/acceptability, ethics, infrastructure and support.
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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 | 0.326 | 0.335 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.026 | 0.021 |
| Research integrity | 0.047 | 0.048 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".