Effect of diagnostic score reporting following a structured clinical assessment of dental hygiene student performance.
Bibliographic record
Abstract
Background: Diagnostic score reporting is one method of providing feedback to all students following a structured clinical assessment but its effect on learning has not been studied. The objective of this study was to assess the impact of this feedback on student reflection and performance following a dental hygiene assessment. Methods: In 2016, dental hygiene students at the University of Alberta participated in a mock structured clinical assessment during which they were randomly assigned to receive a diagnostic score report (intervention group) or an overall percentage grade of performance (control group). The students later reflected upon their performance and took their regularly scheduled structured clinical assessment. Reflections underwent content analysis by diagnostic domains (eliciting essential information, effective communication, client-centred care, and interpreting findings). Results were analysed for group differences. Results: > 0.05. Discussion: Students who received diagnostic score reporting appeared to reflect more accurately upon their weaknesses. However, this knowledge did not translate into improved performance. Modifications and enhancements to the report may be necessary before an effect on performance will be seen. Conclusion: Diagnostic score reporting is a promising feedback method that may aid student reflection. More research is needed to determine if these reports can improve performance.
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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 | 0.011 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".