Assessment for selection for the health care professions and specialty training: Consensus statement and recommendations from the Ottawa 2010 Conference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 it