Improving Treatment of Elderly Patients by Interprofessional Education in a Quality Network of Geriatric Medicine: Protocol for Evaluating an Educational Initiative
Notice bibliographique
Résumé
BACKGROUND: All statistics on the development of demand for care for multimorbid elderly patients highlight the acute pressure to act to adequately respond to the expected increase in geriatric patient population in the next 15 years. Against this background, great importance must be attached to the improvement of cross-occupational group and cross-sector treatment of these patients. In addition, many professionals in the health care sector often have little knowledge about the special treatment and care needs of the elderly. OBJECTIVE: The Quality Network of Geriatric Medicine in north-west Germany is the body responsible for the project; with its member organizations, it provides care for over 400,000 inpatients and is thus one of the largest associations for geriatrics in Germany. The Quality Network conducts binding evaluated qualification measures for staff involved in the treatment and care of multimorbid elderly patients. The training offers are especially intended for staff who have not yet been trained in working with elderly patients. This approach is intended to improve the expertise of various occupational groups on different hierarchy levels, to include patients and their family members in the evaluation process, and to initiate changes within the organizations. METHODS: Various instruments are used in the evaluation of qualification measures: besides written surveys and questionnaires, structured work groups (consensus groups) and interviews are conducted. The evaluation starts before the qualification measures to determine the starting point and then continues during the measure and after its completion. This allows major findings to be integrated directly into the ongoing qualification program. At least 100 trainings on geriatric topics, 80 consensus groups, and 120 patients (and family members) are going to be included in the study. RESULTS: The evaluation of the educational initiative is funded by the State of Northrhine-Westfalia (Germany; LZG TG 71 001 / 2015 and LZG TG 71 002 / 2015). The results of the study will be published after review and approval by the state authorities - presumably by the end of 2019. The before and after comparison of the treatment-related outcomes at the beginning and near the completion of the educational initiative gives insights into how transfer-oriented education can improve the treatment of elderly patients across sector lines for inpatients as well as outpatients. The evaluation of the implementation of educational content in day-to-day work and occupational groups is to facilitate recommendations about economically sensible use of educational resources and about further adjustments to the training content. CONCLUSIONS: The evaluation develops the foundation for targeted and needs-oriented qualification measures as well as transfer in cross-sector, multiprofessional networks. Instruments and results will be published and provided to other health care networks and institutions. The Quality Network will implement the results of the evaluation process in its member institutions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11067.
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 | 0,073 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,007 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,034 | 0,008 |
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 ».