Balancing Reflection and Validity in Health Profession Students’ Self-Assessment
Bibliographic record
Abstract
Students and practitioners in self-regulating health professions are expected to engage in reflective, valid self-assessment activities. However, self-assessment processes can be flawed. People may have a limited understanding of the critical thinking needed to reflect on their performance and they may over-estimate or under-estimate their abilities. This article highlights educational approaches that can help students achieve a balance of reflecting critically and developing more accurate self-assessments. Considerations involved in defining self-assessment are identified. Explanations of how integrating reflection requires critical thinking; information from both internal and external sources; and incidental learning are provided. Suggestions for addressing validity by recognizing that inaccuracies exist; knowing that people‘s history with academic success can impact their self-assessments; and creating links to affective outcomes are offered. Emphasis is placed on viewing self-assessment as a formative learning activity that is introduced early and consistently in health education programs.
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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 | 0.417 | 0.561 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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 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".