Tensions in Informed Self-Assessment: How the Desire for Feedback and Reticence to Collect and Use It Can Conflict
Bibliographic record
Abstract
PURPOSE: Informed self-assessment describes the set of processes through which individuals use external and internal data to generate an appraisal of their own abilities. The purpose of this project was to explore the tensions described by learners and professionals when informing their self-assessments of clinical performance. METHOD: This 2008 qualitative study was guided by principles of grounded theory. Eight programs in five countries across undergraduate, postgraduate, and continuing medical education were purposively sampled. Seventeen focus groups were held (134 participants). Detailed analyses were conducted iteratively to understand themes and relationships. RESULTS: Participants experienced multiple tensions in informed self-assessment. Three categories of tensions emerged: within people (e.g., wanting feedback, yet fearing disconfirming feedback), between people (e.g., providing genuine feedback yet wanting to preserve relationships), and in the learning/practice environment (e.g., engaging in authentic self-assessment activities versus "playing the evaluation game"). Tensions were ongoing, contextual, and dynamic; they prevailed across participant groups, infusing all components of informed self-assessment. They also were present in varied contexts and at all levels of learners and practicing physicians. CONCLUSIONS: Multiple tensions, requiring ongoing negotiation and renegotiation, are inherent in informed self-assessment. Tensions are both intraindividual and interindividual and they are culturally situated, reflecting both professional and institutional influences. Social learning theories (social cognitive theory) and sociocultural theories of learning (situated learning and communities of practice) may inform our understanding and interpretation of the study findings. The findings suggest that educational interventions should be directed at individual, collective, and institutional cultural levels. Implications for practice are presented.
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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 | 0.185 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".