Health technologies for the prevention and detection of falls in adult hospital inpatients: a scoping review
Notice bibliographique
Résumé
OBJECTIVE: The objective of this scoping review was to examine and map the evidence relating to the reporting and evaluation of technologies for the prevention and detection of falls in adult hospital inpatients. INTRODUCTION: Falls are a common cause of accidental injury, leading to significant safety issues in hospitals globally, and resulting in substantial human and economic costs. Previous research has focused on community settings with less emphasis on hospital settings. INCLUSION CRITERIA: Participants included adult inpatients, aged 18 years and over; the concept included the use of fall-prevention or fall-detection technologies; the context included any hospital ward setting. METHODS: This scoping review was conducted according to JBI methodology for scoping reviews, guided by an a priori protocol. A wide selection of databases including MEDLINE, CINAHL, AMED, Embase, PEDro, Epistimonikos, and Science Direct were searched for records from inception to October 2019. Other sources included gray literature, trial registers, government health department websites, and websites of professional bodies. Only studies in the English language were included. A three-step search strategy was employed, with all records exported for subsequent title and abstract screening prior to full-text screening. Screening was performed by two independent reviewers and data extraction by one reviewer following agreement checks. Data are presented in narrative and tabular form. RESULTS: Over 13,000 records were identified with 404 included in the scoping review: 336 reported on fall-prevention technologies, 51 targeted detection, and 17 concerned both. The largest contributions of studies came from the USA (n=185), Australia (n=65), the UK (n=36), and Canada (n=18). There was a variety of study designs including 77 prospective cohort studies, 33 before-after studies, and 35 systematic reviews; however, relatively few randomized controlled trials were conducted (n = 25). The majority of records reported on multifactorial and multicomponent technologies (n = 178), followed by fall detection devices (n = 86). Few studies reported on the following interventions in isolation: fall risk assessment (n = 6), environment design (n = 8), sitters (n = 5), rounding (n = 3), exercise (n = 3), medical/pharmaceutical (n = 2), physiotherapy (n = 1), and nutritional (n = 1). The majority (57%) of studies reported clinical effectiveness outcomes, with smaller numbers (14%) reporting feasibility and/or acceptability outcomes, or cost-effectiveness outcomes (5%). CONCLUSIONS: This review has mapped the literature on fall-prevention and fall-detection technology and outcomes for adults in the hospital setting. Despite the volume of available literature, there remains a need for further high-quality research on fall-prevention and fall-detection technologies.
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 | 0,026 | 0,087 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,023 | 0,020 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».