The Processes and Dimensions of Informed Self-Assessment: A Conceptual Model
Bibliographic record
Abstract
PURPOSE: To determine how learners and physicians engaged in various structured interventions to inform self-assessment, how they perceived and used self-assessment in clinical learning and practice, and the components and processes comprising informed self-assessment and factors that influence these. METHOD: This was a qualitative study guided by principles of grounded theory. Using purposive sampling, eight programs were selected in Canada, the United States, the United Kingdom, the Netherlands, and Belgium, representing low, medium, and high degrees of structure/rigor in self-assessment activities. In 2008, 17 focus groups were conducted with 134 participants (53 undergraduate learners, 32 postgraduate learners, 49 physicians). Focus-group transcripts were analyzed interactively and iteratively by the research team to identify themes and compare and confirm findings. RESULTS: Informed self-assessment appeared as a flexible, dynamic process of accessing, interpreting, and responding to varied external and internal data. It was characterized by multiple tensions arising from complex interactions among competing internal and external data and multiple influencing conditions. The complex process was evident across the continuum of medical education and practice. A conceptual model of informed self-assessment emerged. CONCLUSIONS: Central challenges to informing self-assessment are the dynamic interrelationships and underlying tensions among the components comprising self-assessment. Realizing this increases understanding of why self-assessment accuracy seems frequently unreliable. Findings suggest the need for attention to the varied influencing conditions and inherent tensions to progress in understanding self-assessment, how it is informed, and its role in self-directed learning and professional self-regulation. Informed self-assessment is a multidimensional, complex construct requiring further research.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".