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Development and Validation of a Meta-Instrument for Nursing Assessment in Adult Hospitalization Units (VALENF Instrument) (Part I).

David Luna-AleixosIrene Llagostera-ReverterXimo Castelló-BenaventMarta Aquilué-BallaríGema Mecho-MontoliuAgueda Cervera GaschMaría Jesús Valero-ChillerónDesirée Mena-TudelaLaura Andreu PejóRafael Martínez-GonzálbezVíctor Manuel Gonzalez-Chordá
Published in: International journal of environmental research and public health (2022)
Nursing assessment is the basis for performing interventions that match patient needs, but nurses perceive it as an administrative load. This research aims to develop and validate a meta-instrument that integrates the assessment of functional capacity, risk of pressure ulcers and risk of falling with a more parsimonious approach to nursing assessment in adult hospitalization units. Specifically, this manuscript presents the results of the development of this meta-instrument (VALENF instrument). A cross-sectional study based on recorded data was carried out in a sample of 1352 nursing assessments. Socio-demographic variables and assessments of Barthel, Braden and Downton indices at the time of admission were included. The meta-instrument's development process includes: (i) nominal group; (ii) correlation analysis; (iii) multiple linear regressions models; (iv) reliability analysis. A seven-item solution showed a high predictive capacity with Barthel (R 2 adj = 0.938), Braden (R 2 adj = 0.926) and Downton (R 2 adj = 0.921) indices. Likewise, reliability was significant ( p < 0.001) for Barthel (ICC = 0.969; τ-b = 0.850), Braden (ICC = 0.943; τ-b = 0.842) and Downton (ICC = 0.905; κ = 7.17) indices. VALENF instrument has an adequate predictive capacity and reliability to assess the level of functional capacity, risk of pressure injuries and risk of falls.
Keyphrases
  • mental health
  • patient reported outcomes
  • healthcare
  • quality improvement
  • emergency department
  • physical activity
  • case report
  • machine learning
  • community dwelling
  • data analysis
  • clinical evaluation