Assessment for selection for the health care professions and specialty training: Consensus statement and recommendations from the Ottawa 2010 Conference
Bibliographic record
Abstract
Assessment for selection in medicine and the health professions should follow the same quality assurance processes as in-course assessment. The literature on selection is limited and is not strongly theoretical or conceptual. For written testing, there is evidence of the predictive validity of Medical College Admission Test (MCAT) for medical school and licensing examination performance. There is also evidence for the predictive validity of grade point average, particularly in combination with MCAT for graduate entry but little evidence about the predictive validity of school leaver scores. Interviews have not been shown to be robust selection measures. Studies of multiple mini-interviews have indicated good predictive validity and reliability. Of other measures used in selection, only the growing interest in personality testing appears to warrant future work. Widening access to medical and health professional programmes is an increasing priority and relates to the social accountability mandate of medical and health professional schools. While traditional selection measures do discriminate against various population groups, there is little evidence on the effect of non-traditional measures in widening access. Preparation and outreach programmes show most promise. In summary, the areas of consensus for assessment for selection are small in number. Recommendations for future action focus on the adoption of principles of good assessment and curriculum alignment, use of multi-method programmatic approaches, development of interdisciplinary frameworks and utilisation of sophisticated measurement models. The social accountability mandate of medical and health professional schools demands that social inclusion, workforce issues and widening of access are embedded in the principles of good assessment for selection.
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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 | 0.226 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.027 | 0.016 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".