Daily Encounter Cards—Evaluating the Quality of Documented Assessments
Bibliographic record
Abstract
BACKGROUND: Concerns over the quality of work-based assessment (WBA) completion has resulted in faculty development and rater training initiatives. Daily encounter cards (DECs) are a common form of WBA used in ambulatory care and shift work settings. A tool is needed to evaluate initiatives aimed at improving the quality of completion of this widely used form of WBA. OBJECTIVE: The completed clinical evaluation report rating (CCERR) was designed to provide a measure of the quality of documented assessments on in-training evaluation reports. The purpose of this study was to provide validity evidence to support using the CCERR to assess the quality of DEC completion. METHODS: Six experts in resident assessment grouped 60 DECs into 3 quality categories (high, average, and poor) based on how informative each DEC was for reporting judgments of the resident's performance. Eight supervisors (blinded to the expert groupings) scored the 10 most representative DECs in each group using the CCERR. Mean scores were compared to determine if the CCERR could discriminate based on DEC quality. RESULTS: < .001). A generalizability analysis demonstrated the majority of score variation was due to differences in DECs. The reliability with a single rater was 0.95. CONCLUSIONS: The CCERR is a reliable and valid tool to evaluate DEC quality. It can serve as an outcome measure for studying interventions targeted at improving the quality of assessments documented on DECs.
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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 | 0.053 | 0.182 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".