Bibliographic record
Abstract
Self-assessment and its role in self-regulation and lifelong learning lack clarity. A goal of this Journal of Continuing Education in the Health Professions issue is to begin to clarify our current understanding of self-assessment and what it entails, as seen through an educational lens. The purpose of this summary article is to synthesize briefly the definitions of self-assessment proposed by the authors, their perspectives on external and internal factors influencing and/or inherent in self-assessment, and common messages for educational research and practice. Among the seven authors, there appears to be unanimity in conceptualizing self-assessment within a formative, educational perspective, and seeing it as an activity that draws upon both external and internal data, standards, and resources to inform and make decisions about one's performance. Multiple external sources can and should inform self-assessment, perhaps most important among them performance standards, eg, clinical practice guidelines, and use of formal practice audit and feedback approaches. Equally important, internal factors or capacities also influence one's ability to self-assess and self-monitor, such as reflection, mindfulness, openness, curiosity. In summary, these articles aid in our appreciation of the complexity of self-assessment as a formative activity and identify multiple implications for educational practice and research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.078 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.068 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.008 | 0.026 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".