Digital Literacy Training for Digitalization Officers (“Digi-Managers”) in Outpatient Medical and Psychotherapeutic Care: Conceptualization and Longitudinal Evaluation of a Certificate Course
Bibliographic record
Abstract
Background: Digital tools, services, and information in patient care demand new competencies in outpatient care, and the workforce is faced with the need to deal with digitalization. Objective: In a targeted certificate course (Certification of Digitalization Officers in Medical Practices and Psychotherapeutic Practices, Digi-Manager), medical assistants are trained to serve as digitalization officers, enabling them to implement the requirements of digitalized health care within their practices. Methods: As part of an accompanying study, the course is evaluated by the participants, and the change in their digital literacy is recorded. We measured different knowledge, skills, and attitude dimensions at 3 different times-before, during, and after the course-and used ANOVA to examine significant changes. Results: Digi-Managers started the course with an already high self-assessment of their digital literacy. Skills and knowledge increased significantly in all categories (cognitive, technical, ethical, and health information) from the initial to the final measurement, as did self-confidence in the use of general software and hardware. Positive attitude remained stable over the training period, and the course was rated very positively by participants across all areas. Conclusions: Training programs on digital topics for health care professionals are necessary, and this certification course is a role model for successful further education through a mixture of theoretical knowledge transfer and practical application. Especially, the use of a digital maturity model and a digital laboratory was a unique and useful feature. Further research needs to go into alternative assessment methods of digital literacy, as the results suggest that self-assessment measures self-efficacy and confidence, rather than pure competence. Nevertheless, the increase in self-assessed competence suggests that the training was successful.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".