Dedicated Assessors: description of an innovative education intervention to facilitate direct observation in the clinical setting
Bibliographic record
Abstract
BACKGROUND: The Department of Pediatrics at Queen's University undertook a pilot project in July 2017 to increase the frequency of direct observations (DO) its residents received without affecting the patient flow in a busy hospital-based pediatric ambulatory care clinic. Facilitating DO for authentic workplace-based assessments is essential for assessing resident's core competencies. The purpose of this study was to pilot an innovative education intervention to address the challenge of implementing DO in the clinical setting. METHODS: The project allowed for staff physicians to act as "dedicated assessors" (DA), a faculty member who was scheduled to conduct direct observations of trainees' clinical skills, while not acting as the attending physician on duty. At the end of the project, focus group interviews were conducted with faculty and residents, and thematic analysis was completed. RESULTS: Participants reported an increase in the overall quality of feedback received during the observations performed by a DA, with more specific feedback and a broader focus of assessment. There seemed to be little disruption to patient care. Some residents described the observations as anxiety-provoking. CONCLUSIONS: Overall, this project provides insight into an educational approach that medical residency programs can apply to increase the frequency of workplace-based DO and boost the quality of feedback residents receive while maintaining the flow of already busy ambulatory care clinics.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".