Bibliographic record
Abstract
A proposal which would see clinical year Medicine students at Australian universities be tested on their critical reasoning skills to accurately manage patients has won University of Notre Dame academic, Dr Michael Wan, first prize at an international medical education conference. Dr Wan, Head of Assessment (Medical Education Unit) at the School of Medicine, Sydney Campus, submitted an abstract titled: Using script concordance testing as a new modality of assessment for graduate entry medical students – a pilot study, to the 6th Congress of Asian Medical Education Association (AMEA). He was awarded First Prize for Best Poster Presentation by a panel of international judges at the Congress held in Kuala Lumpur, Malaysia, earlier this year. Script concordance testing is a new modality of assessment in medical education where third and fourth year medical students are given a clinical scenario. They are then provided with additional background information to determine the likelihood of a particular diagnosis or the appropriateness of various investigations and management options. The new model tests a student’s clinical reasoning and problem solving ability and how they apply this to ‘real-life’ scenarios. Script concordance testing draws out a student's ability to think and reason like a medical professional. Prior to the assessment, a panel comprising several medical professionals will provide individual answers to the same questions given to the clinical year students. Should the students’ answers to an assessment question concur with the majority of specialists in the panel, they are given full marks. If their answer reflects a minority of specialists with a different opinion, students are still given a fraction of the total mark for that question. The UNDA School of Medicine, Sydney, the University of Adelaide and the University of Montreal have collaborated to develop and implement the new script concordance testing questions for clinical year medical students. Notre Dame’s Sydney Campus is one of the first universities in Australia to trial these types of questions in the assessment process for medical students. “So far the feedback from students has been positive since they love the style of questions being asked as they relate to the clinical encounters that they would see every day in the ward,” Dr Wan said. “The questions are more realistic, but they are also more difficult to answer as there is no absolute right or wrong response and your answer must concur with the views held by the majority of medical specialists.” Associate Dean of Teaching and Learning at the School of Medicine, Sydney Campus, Associate Professor Rosa Canalese, says it’s a great achievement to be recognised for innovation in assessment methodology on a global level. “To be at the forefront in this area is very exciting and to be acknowledged on the international stage is a fantastic achievement for such a young medical school,” Associate Professor Canalese said. Media Contact: Leigh Dawson (+61) 8 9433 0569, Mob (+61) 0405 441 093
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 | 0.050 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.039 |
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".