Securing the Continuity of Medical Competence in Times of Demographic Change: A Proposal
Notice bibliographique
Résumé
BACKGROUND: University hospitals make up the backbone of medical and economic services of hospitals in Germany: they qualify specialist physicians, ensure medical research, and provide highly specialized maximum medical care, which other hospitals cannot undertake. In addition to this assignment, medical research and academic teaching must be managed despite a growing shortage of specialist physicians. By the year 2020, the need for the replacement of retired physicians and increased demand will total 30,000 positions. The situation will become more difficult because, on the whole, patients are becoming older and sicker and because specialist physicians are able to find more attractive working conditions in smaller hospitals, abroad, or outside of curative medicine. OBJECTIVE: In order to retain sufficient qualified employees, major improvements in quality are required in terms of working and training conditions. For this purpose, a sustainable innovation process is necessary, which incorporates solutions from outside of the health care sector in order to be able to learn from experiences and mistakes from other industries. The FacharztPlus project aims to find suitable measures in order to retain specialist physicians for more years after the completion of 5 years of professional training. This should determine the suitability of additional qualifications alongside the professional career and an expertise-related work organization oriented to different stages of life. METHODS: Structured interviews, surveys, and repertory grids are used as preparation for cross-industry expert panels to create future work scenarios for university hospitals. Industries involved are harbor logistics (container terminal), airports, and digitized industrial production ("industry 4.0") because these industries are also facing a shortage of qualified staff and have to respond to rapidly changing demands. Based on the experts' scenarios, consensus groups will be established in each university hospital trying to reach consensus about the implementation of relevant factors in order to improve employee retention. RESULTS: We expect these consensus groups to develop and introduce measures for more structured training procedures, individual and team incentives, organizational guidelines for better recruiting and retention in hospitals, models of flexible and attractive working conditions including shift work and vacation planning, and use of new learning tools (eg, tablet PCs and mobile phones). CONCLUSIONS: All measures are implemented in the Department of Anaesthesiology, Intensive Care, Emergency Care and Pain Medicine at the University Hospital Muenster (UKM) with approximately 150 physicians and in the further 44 departments of the UKM and 22 teaching hospitals, which all together employ more than 5000 physicians. The measures will also be implemented at the university hospitals in Aachen, Rostock, and Greifswald. All decisions and measures will be discussed with representatives from hospital management and professional associations. Results will be presented at conferences and published in journals.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».