What steps are necessary to create written or web-based selected-response assessments?
Bibliographic record
Abstract
Before we work out what constitutes an assessment's value for a given cost in medical education, we must first outline the steps necessary to create an assessment, and then assign a cost to each step. In this study we undertook the first phase of this process: we sought to work out all the steps necessary to create written selected-response assessments. First, the lead author created an initial list of potential steps for developing written assessments. This was then distributed to the other three authors. These authors independently added further steps to the list. The lead author incorporated the contributions of these others and created a second draft. This process was repeated until consensus was achieved amongst the study's authors. Next, the list was shared by means of an online questionnaire with 100 healthcare professionals with experience in medical education. The results of the authors' and healthcare professionals' thoughts and feedback on the steps, needed to create written assessment, are outlined below in full. We outlined the steps that are necessary to create written or web-based selected-response assessments.
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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 | 0.350 | 0.578 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".