Evaluation of the Continuing Education Training “Beratende für Digitale Gesundheitsversorgung” (“Consultant for Digital Healthcare”): Protocol for an Effectiveness Study
Notice bibliographique
Résumé
BACKGROUND: The digital transformation in health care requires training nursing and health professionals in the digitally competent use of digital assistive technologies (DAT). The continuing education training "Beratende für digitale Gesundheitsversorgung" ("Consultant for Digital Healthcare") was developed to fill this gap. The effectiveness of the training program will be assessed in this study. OBJECTIVE: The primary objective is to record and measure the participants' learning success. We will assess whether the previously defined teaching intentions, learning objectives, competencies, and participants' expectations have been achieved and whether a transfer of learning occurred. The secondary objective is participant satisfaction and feasibility of the training. The tertiary objective is the successful transfer of DAT by participants in their institutions. METHODS: Approximately 65 nursing and health care professionals will participate in the pilot phase of the further training and evaluation process, which is planned in a mixed methods design in a nonsequential manner. The different methods will be combined in the interpretation of the results to achieve a synaptic view of the training program. We plan to conduct pre-post surveys in the form of participant self-assessments about dealing with DAT and content-related knowledge levels. Exploratory individual interviews will also be conducted to build theory, to examine whether and to what extent competence (cognition) has increased, and whether dealing (affect) with DAT has changed. Furthermore, an interim evaluation within the framework of the Teaching Analysis Poll (TAP) will occur. The knowledge thereby gained will be used to revise and adapt the modules for future courses. To assess the transfer success, the participants create a practical project, which is carried out within the training framework, observed by the lecturers, and subsequently evaluated and adapted. RESULTS: We expect that the learning objectives for the continuing education training will be met. The attendees are expected to increase their level of digital competence in different skills areas: (1) theoretical knowledge, (2) hands-on skills for planning the application and practical use of DAT, (3) reflective skills and applying ethical and legal considerations in their use, (4) applying all that in a structured process of technology implementation within their practical sphere of work. CONCLUSIONS: The aim of this study and appropriate further training program are to educate nursing and health care professionals in the use of DAT, thereby empowering them for a structured change process toward digitally aided care. This focus gives rise to the following research questions: First, how should further training programs be developed, and which focus is appropriate for addressee-appropriate learning goals, course structure, and general curriculum? Second, how should a training program with this specific content and area be evaluated? Third, what are the conditions to offer a continued program? INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/57860.
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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 | 0,130 | 0,094 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,045 | 0,011 |
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 ».