Re-examining single-moment-in-time high-stakes examinations in specialist training: A critical narrative review
Bibliographic record
Abstract
In this critical narrative review, we challenge the belief that single-moment-in-time high-stakes examinations (SMITHSEx) are an essential component of contemporary specialist training. We explore the arguments both for and against SMITHSEx, examine potential alternatives, and discuss the barriers to change.SMITHSEx are viewed as the "gold standard" assessment of competence but focus excessively on knowledge assessment rather than capturing essential competencies required for safe and competent workplace performance. Contrary to popular belief, regulatory bodies do not mandate SMITHSEx in specialist training. Though acting as significant drivers of learning and professional identity formation, these attributes are not exclusive to SMITHSEx.Skills such as crisis management, procedural skills, professionalism, communication, collaboration, lifelong learning, reflection on practice, and judgement are often overlooked by SMITHSEx. Their inherent design raises questions about the validity and objectivity of SMITHSEx as a measure of workplace competence. They have a detrimental impact on trainee well-being, contributing to burnout and differential attainment.Alternatives to SMITHSEx include continuous low-stakes assessments throughout training, ongoing evaluation of competence in the workplace, and competency-based medical education (CBME) concepts. These aim to provide a more comprehensive and context-specific assessment of trainees' competence while also improving trainee welfare.Specialist training colleges should evolve from exam providers to holistic education sources. Assessments should emphasise essential practical knowledge over trivia, align with clinical practice, aid learning, and be part of a diverse toolkit. Eliminating SMITHSEx from specialist training will foster a competency-based approach, benefiting future medical professionals' well-being and success.
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How this classification was reachedexpand
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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".