The role of the assessor: exploring the clinical supervisor's skill set
Bibliographic record
Abstract
BACKGROUND: Clinical supervisors have several different responsibilities. Although their responsibilities as an assessor are important, little is known about what skill set should be acquired for this role and how to foster their development. Documenting assessor skills to study their acquisition and development is critical. METHODS: A web survey based on the principles of Appreciative Inquiry was distributed to faculty members and residents from a Department of Medicine at a Canadian University. Participants were asked to list five and then to identify five (from a list of 10) characteristics or skills demonstrated by clinical supervisors recognised for their excellent assessment skills. RESULTS: Seventeen per cent of faculty members and 23 per cent of residents completed the survey. Fairness is perceived as a key characteristic of an excellent assessor. Faculty members consider that appropriate medical knowledge and skills are important. Residents expressed the importance of appropriate feedback. Both groups indicated the importance of direct observation as a basis for assessment. DISCUSSION: This study offers preliminary insights into the characteristics of excellent assessors. Given the importance of assessment in the daily activities of clinical supervisors, research efforts should strive to better characterise this role in the hopes of increasing the quality and accuracy of assessment.
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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 | 0.028 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".