Diagnostic Score Reporting for a Dental Hygiene Structured Clinical Assessment
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,053 | 0,095 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,009 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».