Black Ice: Five ways to get a grip on grouped self-assessments of competence for program evaluation
Bibliographic record
Abstract
Self-assessments conducted by individuals when taken together (grouped) provide valid and accurate measures of learning outcomes of the group. This is useful for program evaluation. Grouped self-assessments are simple to understand and construct, easy to implement, relatively accurate, and do not require extensive and complex pre-post testing measures. However, group self-assessments have the potential to be misused. To examine how group self-assessments have been used in medical education, we conducted a search of journal articles published in 2017 and 2018 from eight prominent medical education journals. Twenty-seven (n=27) articles that used self-assessments for program evaluation were selected for data extraction and analysis. We found three main areas where misuse of self-assessments may have resulted in inaccurate measures of learning outcomes: measures of “confidence” or “comfort”, pre-post self-assessments, and the use of ambiguous learning objectives. To prevent future misuse and to build towards more valid and reliable data for program evaluations, we present the following recommendations: measure competence instead of confidence or comfort; use pre-test self-assessments for instructional purposes only (and not for data); ask participants to do the post-intervention self-assessments first followed by retrospective pre-intervention self-assessments afterwards; and use observable, clear, specific learning objectives in the educational intervention that can then be used to create the self-assessment statements.
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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 | 0.593 | 0.697 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.043 | 0.035 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.021 | 0.045 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".