“Directed” self-assessment: Practice and feedback within a social context
Bibliographic record
Abstract
INTRODUCTION: Accurate self-assessment appears to be difficult and, some would propose, even impossible. Recent reviews suggest that peer assessment may be more accurate and that multisource feedback (MSF) may inform self-assessment. We had conducted a series of studies of family physicians in an MSF program including assessments from patients, medical colleagues, and coworkers and self-assessment. Using this body of research, this article explores self-assessment within the social context of multisource feedback and investigates the influence of feedback from peers and others upon self-assessment. METHODS: This is a review article in which we synthesized findings of the series of studies with respect to self-assessment, used conclusions to propose a model for self-assessment within a social context, and suggest practical and research implications. RESULTS: Physicians compared peers' and others' assessment feedback with global self-perceptions of performance. Negative feedback, especially from medical colleagues, that was inconsistent with self-perceptions was not readily reconciled with self-assessments. Multiple internal and environmental factors influenced reconciliation and assimilation of negative feedback. Reflection upon feedback and self-perceptions appeared to be instrumental to reconciliation, and reflection could be facilitated. DISCUSSION: We propose a model of "directed" self-assessment to facilitate the integration of external feedback, especially negative feedback, with self-perceptions and enable its use for practice improvement. Implications for education and research include increasing understanding of ways physicians assimilate external feedback and of the role of educators as facilitators of "directed" self-assessment and self-learning to assist physicians in integrating external feedback.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".