Diagnostic Score Reporting for a Dental Hygiene Structured Clinical Assessment
Bibliographic record
Abstract
Background: Structured clinical assessments (SCAs) are an essential part of health professional education as they capture important information on not only what a student knows, but what they can do. However, this useful information is rarely translated into quality feedback that students can use to reflect upon and improve their clinical performance. Feedback is considered a fundamental component of both learning and professional development, and educators are calling for more and better feedback across the health disciplines. However, issues such as time limitations and test confidentiality make feedback provision for SCAs challenging. Diagnostic score reporting (DSR) presents a possible framework for providing all students with more detailed feedback on their SCA performances. DSR summarizes test performance by the underlying domains of knowledge, skills and/or abilities the test intends to measure, and includes resources for making individual improvements within those domains. DSR does not require the actual test items to be revealed to the students, and can be administered efficiently through online means. As such, DSR has some advantageous as potential feedback mechanism for SCAs. To date, DSR has largely been applied only within the context of large-scale assessment, particularly in primary and secondary education. Additionally, very little research has been conducted on the feedback’s measurable impact on student outcomes. Objectives: To develop a general framework for applying DSR within SCAs; to develop and validate a course-specific diagnostic score report for a dental hygiene SCA; and to evaluate the effect of DSR on student reflection and performance. Methods: A literature-based adapted DSR framework was developed to guide the process of DSR for SCAs. This general framework was then applied towards a dental hygiene history taking SCA at the University of Alberta. This process involved identifying the diagnostic domains of the assessment and linking competencies, test items, and learning resources to those domains. In order to evaluate the effect of DSR on student outcomes, a mock-SCA was developed where half the students were randomly assigned to receive DSR, while the other half received only one overall numerical grade following the assessment. All students were then asked to reflect upon their mock-SCA performance, and later completed their regularly scheduled year-end history taking SCA. The results were collected and analyzed to look for differences between groups on reflection quality, content, and year-end SCA results. Results: Four skills-based domains were identified as necessary to complete the dental hygiene history taking SCA: effective communication, client-centered care, eliciting essential information, and interpreting findings. No differences in reflection quality were found, while reflection content significantly differed by the experimental groups. The DSR group was significantly more likely to report needing to improve on interpreting findings (p = .007), while the control group focused on eliciting information (p = .04). Overall, students tended to perform quite well on eliciting information (M = 92.11%, SD = 9.63%), but poorly on interpreting findings (M = 42.11%, SD = 17.56%). The DSR group did not show significant improvements in their year-end SCA results. Conclusions: DSR appeared to result in improved identification of history taking skills that required improvement, however this improved self-assessment did not translate into improved performance. DSR presents a promising start for improving the feedback students receive following their SCAs, however further enhancements are required. Suggestions for improving the feedback to facilitate behaviour change include: improving the learning resources provided to the students within the report, adding video feedback (self and exemplars), providing a means for students to response to their feedback, and increasing the individualization/personalization of the reports.
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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 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".