Abstract
Background: The MAAS Global (Maastricht History-taking and Advice Scoring List) is an internationally widely-used instrument in under-and postgraduate medical education. The focus is on the assessment of medical communication and clinical skills. The assessment tool, which has also been used in Germany since 2015, has a high-quality design (e.g. comprehensible structure, appropriate complexity), good psycho-metric properties and is very user-friendly. An update of MAAS Global, MAAS 2.0, was published in March 2021 with a new and greater focus on context and the formative. Method: The revised version of the MAAS 2.0 assessment sheet was translated into German with the authors’ permission. Open questions were discussed with the revision process project manager. Results: The revision was carried out with a view to focusing on the patient’s frame of reference, removing ambiguities identified previously while using MAAS Global, and closer alignment with the underlying Calgary-Cambridge model. Furthermore, the scale used for the evaluation was modified and now uses a formative evaluation range without grade-related classification. Conclusion: With the content reorientation of MAAS Global to MAAS 2.0, and the associated focus on frame of reference, context, the formative, the revision presented here sets new priorities for future evaluations in the context of under-and postgraduate medical education and the assessment of medical patient communication in general.
| Translated title of the contribution | From summative MAAS Global to formative MAAS 2.0 – a workshop report |
|---|---|
| Original language | German |
| Article number | Doc9 |
| Journal | GMS Journal for Medical Education |
| Volume | 40 |
| Issue number | 1 |
| Pages (from-to) | Doc9 |
| DOIs | |
| Publication status | Published - 2023 |
Funding
This work was carried out within the context of the LABORATORIUM-project at the University of Luebeck, dealing with the establishment of an AI-based communication learning assistance. Under the project number 16DHBKI075, this work was supported by the German federal-state initiative to promote artificial intelligence in higher education.
