Features of assessment learners use to make informed self‐assessments of clinical performance
Bibliographic record
Abstract
CONTEXT: Conceptualisations of self-assessment are changing as its role in professional development comes to be viewed more broadly as needing to be both externally and internally informed through activities that enable access to and the interpretation and integration of data from external sources. Education programmes use various activities to promote learners' reflection and self-direction, yet we know little about how effective these activities are in 'informing' learners' self-assessments. OBJECTIVES: This study aimed to increase understanding of the specific ways in which undergraduate and postgraduate learners used learning and assessment activities to inform self-assessments of their clinical performance. METHODS: We conducted an international qualitative study using focus groups and drawing on principles of grounded theory. We recruited volunteer participants from three undergraduate and two postgraduate programmes using structured self-assessment activities (e.g. portfolios). We asked learners to describe their perceptions of and experiences with formal and informal activities intended to inform self-assessment. We conducted analysis as a team using a constant comparative process. RESULTS: Eighty-five learners (53 undergraduate, 32 postgraduate) participated in 10 focus groups. Two main findings emerged. Firstly, the perceived effectiveness of formal and informal assessment activities in informing self-assessment appeared to be both person- and context-specific. No curricular activities were considered to be generally effective or ineffective. However, the availability of high-quality performance data and standards was thought to increase the effectiveness of an activity in informing self-assessment. Secondly, the fostering and informing of self-assessment was believed to require credible and engaged supervisors. CONCLUSIONS: Several contextual and personal conditions consistently influenced learners' perceptions of the extent to which assessment activities were useful in informing self-assessments of performance. Although learners are not guaranteed to be accurate in their perceptions of which factors influence their efforts to improve performance, their perceptions must be taken into account; assessment strategies that are perceived as providing untrustworthy information can be anticipated to have negligible impact.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".