Development of diagnostic score reporting for a dental hygiene structured clinical assessment.
Bibliographic record
Abstract
Background: Structured clinical assessments capture key information about performance that is rarely shared with the student as feedback. The purpose of this review is to describe a general framework for applying diagnostic score reporting within the context of a structured clinical assessment and to demonstrate that framework within dental hygiene. Methods: The framework was developed using current research in the areas of structured clinical assessments, test development, feedback in higher education, and diagnostic score reporting. An assessment blueprint establishes valid diagnostic domains by linking clinical competencies and test items to the domains (e.g., knowledge or skills) the assessment intends to measure. Domain scores can be given to students as reports that identify strengths and weaknesses and provide information on how to improve. Results: The framework for diagnostic score reporting was applied to a dental hygiene structured clinical assessment at the University of Alberta in 2016. Canadian dental hygiene entry-to-practice competencies guided the assessment blueprinting process, and a modified Delphi technique was used to validate the blueprint. The final report identified 4 competency-based skills relevant to the examination: effective communication, client-centred care, eliciting essential information, and interpreting findings. Students received reports on their performance within each domain. Discussion: Diagnostic score reporting has the potential to solve many of the issues faced by administrators, such as item confidentiality and the time-consuming nature of providing individual feedback. Conclusion: Diagnostic score reporting offers a promising framework for providing valid and timely feedback to all students following a structured clinical 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 | 0.232 | 0.380 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".