Educational Needs in Geriatric Medicine Among Health Care Professionals and Medical Students in COST Action 21122 PROGRAMMING: Mixed-Methods Survey Protocol
Notice bibliographique
Résumé
BACKGROUND: The European Cooperation in Science and Technology (COST) Action 21122, PROmoting GeRiAtric Medicine in countries where it is still eMergING (PROGRAMMING) developed an online open survey to assess the educational interests and needs of health care professionals and final-year medical students across participating countries. This survey aims to establish a current baseline for developing educational content on geriatric medicine for nongeriatricians and a framework for its delivery. OBJECTIVE: This paper describes the aim, development, structure, content, and dissemination of this survey. METHODS: The mixed methods electronic survey, initially developed in English through a cocreation process with key stakeholders, was subsequently translated into 24 languages. It received ethics approval from multiple participating countries. Within- and cross-country analyses of the survey data will be conducted using descriptive and inferential statistics for quantitative data and content analyses for qualitative data. National and international teams will conduct analyses in parallel exploring responses within a specific country or region, professional category (or among medical students), or setting of work. Basic descriptive statistics and chi-square tests will evaluate differences in knowledge, relevance, and interest in geriatric topics across countries, professions, and settings of work. The effectiveness of formal education in geriatric medicine and clinical rotations in geriatric settings versus the lack thereof in promoting higher self-perceived knowledge on geriatric medicine topics will be explored using binary logistic regression. We will provide basic descriptive statistics (frequencies) of reported barriers to receiving further training in geriatric medicine and the effectiveness of various teaching methods as rated by the respondents and explore differences across countries, professions, and settings using chi-square tests. We will conduct qualitative content analyses of free-text responses to the questions exploring professionals' and medical students' thoughts on caring for older people and medical students' thoughts on becoming geriatricians. RESULTS: The survey included the following sections: Informed Consent, Demographics, Topics and Skills, Medical Students vs. Professionals, Current Profession (for professionals), Previous Education in Geriatric Medicine (for professionals), Education in Geriatric Medicine (for medical students), Interest in Care of Older People or Geriatric Medicine, Suggestions for Courses in Care for Older People or Geriatric Medicine, and Closure. The survey was disseminated between October 9, 2023, and June 5, 2024, and received 6099 responses; after cleaning, there were 5922 (97.1%) responses (n=5474, 92.43% from professionals and n=448, 7.57% from medical students). CONCLUSIONS: This survey's findings will inform educational projects across the PROGRAMMING countries. We will share these findings with national and international stakeholders, including professional societies, medical schools, and other relevant organizations. We will advocate for professional educational curricula to include geriatric topics rated as relevant by the survey respondents and promote clinical rotations in geriatric settings and teaching methods rated as effective by the survey respondents. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64985.
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 | 0,030 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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 ».