Assessment in Medical Education; What Are We Trying to Achieve?
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
Within the arena of medical education, it is generally acknowledged that assessment drives learning. Assessment is one of the most significant influences on a student’s experience of higher education and improving assessment has a huge impact on the quality of learning (Liu, N. and Carless, D, 2006). Ideally we want to enhance student’s capacity for learning and engagement with the curriculum (ACGME Outcome Project, 2000). However, this doesn’t always happen as it is heavily dependent on the form of assessment used and whether or not timely comprehensive feedback is given. This paper focuses on the challenges associated with assessment in medical education and looks at the current trends. Well-designed formative assessment can focus students on effective learning and divert them away from summative assessment, which focuses attention on grades and reproductive thinking (Liu, N. and Carless, D, 2006). Whether one decides to utilise summative or formative assessment methods, both methods of assessment are useful when applied in the correct setting and at an appropriate stage of learning. It is apparent that assessment is the gatekeeper of higher learning and we need to embrace new methods of assessment in order to meet the challenges associated with ‘Generation Y’. Novel assessment methods such as self and peer assessment are growing in popularity. Students who participate in these forms of assessment may initially feel that it is challenging but worthwhile overall, as it helps to develop their critical thinking skills. Incorporating complimentary assessment components could benefit student’s learning without sacrificing the integrity of the curriculum.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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