Expanding the Design Space for Fall Prevention in Acute Orthopedic Hospital Care: Human-Centered Design Study
Notice bibliographique
Résumé
BACKGROUND: In-hospital fall prevention is a complex phenomenon most efficiently addressed via a wide range of multifactorial interventions. Technology may contribute, but research in this field has so far mainly focused on detecting falls. As a result, new knowledge from a system perspective is needed regarding when and how new technologies may support fall prevention among patients who have been hospitalized. OBJECTIVE: This study aimed to explore and describe clinical practices in an acute orthopedic hospital ward for fall prevention from a system perspective; determine the needs and possibilities related to support for clinical practices for fall prevention; and test whether a framework for studying interactions between people, activities, contexts, and technologies can be used to support observations of complex phenomena such as clinical fall prevention. METHODS: This qualitative study followed the principles of human-centered design while combining focused ethnography with a workshop. Eight health care professionals representing different staff categories in an acute hospital ward of an orthopedic clinic participated in on-site interviews or were observed in their clinical practice. Data from these events were subjected to qualitative content analysis to describe the clinical practices for fall prevention observed in terms of people, activities, context, and tools. In a workshop, a larger group of clinic personnel provided their views on fall prevention, described the activities and tools they observed to prevent falls, and discussed needs for further support. RESULTS: This study determined that health personnel considered fall prevention in all their interactions with patients, which included a wide range of activities for fall prevention wherein staff categories played complementary roles. These staff-patient meetings were goal oriented, responsive, and patient centered. The staff often served as key "tools" in assessment, communication, and coaching, while digital tools (mainly computer-based software programs) were used for information retrieval, documentation, and communication. The personnel worked to prevent patient falls both during hospitalization and after discharge. They believed that the long-term perspective was much more difficult to address in their clinical practice, and they expressed a need for more homelike environments in the hospital. CONCLUSIONS: The view on technology-based in-hospital fall prevention can be broadened not only to mainly include monitoring and alarm systems, information systems in general, or computer-based information in particular systems but also to support activities performed by health personnel that engage patients in fall prevention. For example, tools such as these can be implemented in training involving daily activities and mobility within safe yet more homelike clinical contexts.
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 | 0,058 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».