Assessment of clinical competence in competency-based education.
Bibliographic record
Abstract
Objective: The purpose of this review is to explore the literature on continuous assessment in the evaluation of clinical competence, to examine the variables influencing the assessment of clinical competence, and to consider the impact of high-stakes summative assessment practices on student experiences, learning, and achievement. Methods: A literature search of CINAHL, PubMed, ERIC (EBSCO), Education Source, and Google Scholar was conducted using key terms. Articles reviewed were limited to full-text, peer-reviewed articles published in English from 2000 to 2019. Selected articles for this review include a meta-analysis, systematic reviews, and studies using qualitative and quantitative designs. Results: Findings reveal that current assessment practices such as one-time high-stakes assessments in the evaluation of clinical competence are influenced by several variables: interexaminer differences in evaluation, variability with non-standardized client use in assessment, the failure to fail, and the impact of stress on performance outcomes. This literature review also highlights a programmatic assessment approach in which student competence is determined by a multitude of low-stakes assessments over time. Conclusion: A review of the literature has highlighted current methods of clinical assessment relying on traditional, summative forms of evaluation, with reliability and validity of the assessment influenced by several variables. Emotions and student experiences related to one-time high-stakes summative assessments may negatively affect student learning and achievement outcomes. The design, implementation, and use of assessment practices within a competency-based education framework warrants further consideration so that optimal assessment for learning practices may be emphasized to enhance student learning and achievement.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".