Development of a learning module for emergency department nurses to improve geriatric knowledge to guide geriatric patient care
Notice bibliographique
Résumé
Background: Canada's population is aging, and the geriatric patient population utilizes emergency departments (ED) at increasingly high numbers. Older patients (65 +) often present to the ED with complex atypical disease presentations, putting them at a higher risk for morbidity and mortality if not recognized. Since the ED model is traditionally designed for rapid assessment and triage, providing effective care to older adults presents many challenges. ED nurses are expected to detect frailty and vulnerability and recognize atypical symptoms of a hidden disease (i.e., geriatric syndromes). However, it is difficult to ensure the delivery of consistent assessments and care in the ED without specific geriatric knowledge and education. Purpose: The purpose of this practicum was to develop a geriatric learning module (GLM) to improve ED nurses' geriatric knowledge to better guide patient care and assessment. Methods: The four methods consisted of 1) an integrated review to identify factors influencing nurses' ability to care for and address the complex needs of older adults visiting the ED and to identify potential strategies to enhance nurses' knowledge and overall geriatric care in the ED, 2) an environmental scan to determine what geriatric resources and policies were available, 3) consultations with ED nurses to identify practice and knowledge issues related to the care of geriatric patients in the ED, and identify educational needs through a questionnaire, 4) the development of the GLM. Results: The literature review identified individual and work environment level factors influencing the care of older adults in ED. Individual-level nursing factors included knowledge gaps, limited experience, negative perceptions, and attitudes. A lack of appropriate physical space and equipment, workload and staff shortages, and ED culture were work environment-level factors. The strategies to improve geriatric care and assessment included education, Geriatric Emergency Management (GEM) nurses, focused nursing assessments, and Geriatric Emergency Department Intervention (GEDI) (i.e., multidisciplinary teams). The environmental scan strengthened the review findings and provided additional information about assessment methods, geriatric educational resources, and the educational needs of nurses. During the consultations, the nurses identified perceived barriers to providing quality care to older ED patients consistent with the literature. Based on the integrated literature review findings, environmental scan, and consultations, a GLM containing six modules and three case studies was developed. Conclusion: The GLM was developed to address the learning needs of the ED nurses and provide the foundational geriatric knowledge and skills to guide geriatric triage, assessment, and care to help improve outcomes for older patients in the ED. The GLM will be incorporated into the onboarding and orientation of nurses joining the ED team.
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 | 0,006 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,003 |
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 ».