Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data. - Wikidata - awgldk - TriplyDB

Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.

Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.

http://www.wikidata.org/entity/Q31067389

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Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.

http://www.wikidata.org/entity/Q31067389

description

2016 nî lūn-bûn

@nan

2016 թուականի Յունուարին հրատարակուած գիտական յօդուած

@hyw

2016 թվականի հունվարին հրատարակված գիտական հոդված

@hy

2016年の論文

@ja

2016年論文

@yue

2016年論文

@zh-hant

2016年論文

@zh-hk

2016年論文

@zh-mo

2016年論文

@zh-tw

2016年论文

@wuu

name

Construction and evaluation of ...... inpatients from nursing data.

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Construction and evaluation of ...... inpatients from nursing data.

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type

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Construction and evaluation of ...... inpatients from nursing data.

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Construction and evaluation of ...... inpatients from nursing data.

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Construction and evaluation of ...... inpatients from nursing data.

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Construction and evaluation of ...... inpatients from nursing data.

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Japan Journal of Nursing Science

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Construction and evaluation of ...... inpatients from nursing data.

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Kazuhiko Ohe

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Shinichiroh Yokota

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P2860

A new look at the statistical model identification

A case-control study of patient, medication, and care-related risk factors for inpatient falls.

Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients.

Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies

Development of the common data base for the FICSIT trials.

Fall prediction in inpatients by bedside nurses using the St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) instrument: a multicenter study.

Evaluation of the Morse Fall Scale: applicability in Chinese hospital populations.

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247-255

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scholarly article

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10.1111/JJNS.12103

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2

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13

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2016-01-19T00:00:00Z

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27040735

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