Desarrollo y validación de meta-instrumentos de mediciónuna aproximación metodológica

  1. Irene Llagostera-Reverter 1
  2. David Luna-Aleixós 2
  3. María Jesús Valero-Chillerón 1
  4. Víctor M. González-Chordá 1
  1. 1 Grupo de Investigación en Enfermería (GIENF-241), Universitat JaumeI, Castellón, España
  2. 2 Grupo de Investigación eNursys, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), Valencia, España
Revue:
Enfermería clínica

ISSN: 1130-8621

Année de publication: 2024

Volumen: 34

Número: 4

Pages: 322-329

Type: Article

DOI: 10.1016/J.ENFCLI.2024.04.002 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Enfermería clínica

Résumé

A valid and reliable nursing assessment is essential for identifying required care and ensuring patient safety. The convenience of conducting a comprehensive assessment of the patient has led to a significant increase in assessment tools that may slow down the process. Nevertheless, the possibility of consolidating various instruments that measure common or similar constructs into a meta-instrument is considered an alternative that could enhance assessment efficiency. A meta-instrument can be defined as a measurement tool that consolidates other instruments based on measuring related constructs and sharing dimensions or items, aiming to achieve a more parsimonious measurement. Literature on such assessment tools is scarce, and there are numerous options for their construction and initial validation. Additionally, it is advisable to confirm their psychometric properties and ensure that they maintain, at the very least, the same diagnostic capacity as the original instruments. This article presents a proposal for the phases to follow in constructing meta-instruments, along with various methodological alternatives that can be employed based on the characteristics of the original instruments and the purpose of creating the meta-instrument. Furthermore, special attention is given to the checklists that should be used to study the psychometric properties and diagnostic capacity of the meta-instruments. Finally, future lines of research and challenges in the development of nursing assessment meta-instruments are discussed.

€ Voir financement

Information sur le financement

Financeurs

Références bibliographiques

  • M.J. Rothman, A.B. Solinger, S.I. Rothman, G.D. Finlay Clinical implications and validity of nursing assessments: A longitudinal measure of patient condition from analysis of the Electronic Medical Record BMJ Open., 2 (2012), p. e000646 https://doi.org/10.1136/bmjopen-2012-000849
  • S. Lorente, C. Viladrich, J. Vives, J.M. Losilla Tools to assess the measurement properties of quality of life instruments: A meta-review BMJ Open., 10 (2020), p. e036038 https://doi.org/10.1136/bmjopen-2019-036038
  • J.A. Brown, A.L. Cooper, M.A. Albrecht Development and content validation of the Burden of Documentation for Nurses and Midwives (BurDoNsaM) survey J Adv Nurs., 76 (2020), pp. 1273-1281 https://doi.org/10.1111/jan.14320
  • A. Palese, E. Marini, A. Guarnier, P. Barelli, P. Zambiasi, E. Allegrini, et al. Overcoming redundancies in bedside nursing assessments by validating a parsimonious meta-tool: Findings from a methodological exercise study J Eval Clin Pract., 22 (2016), pp. 771-780 https://doi.org/10.1111/jep.12539
  • M. Swietlik, P.P. Sengstack An evaluation of nursing admission assessment documentation to identify opportunities for burden reduction J Inform Nurs., 5 (2020), pp. 6-11
  • J. Lindo, R. Stennett, K. Stephenson-Wilson, K.A. Barrett, D. Bunnaman, P. Anderson-Johnson, et al. An audit of nursing documentation at three public hospitals in Jamaica J Nurs Scholarsh., 48 (2016), pp. 499-507 https://doi.org/10.1111/jnu.12234
  • A. Iula, C. Ialungo, C. de Waure, M. Raponi, M. Burgazzoli, M. Zega, et al. Quality of care: Ecological study for the evaluation of completeness and accuracy in nursing assessment Int J Environ Res Public Health., 17 (2020), p. 3259 https://doi.org/10.3390/ijerph17093259
  • B. Munroe, K. Curtis, J. Considine, T. Buckley The impact structured patient assessment frameworks have on patient care: An integrative review J Clin Nurs., 22 (2013), pp. 2991-3005 https://doi.org/10.1111/jocn.12226
  • D. Luna-Aleixos, V.M. González-Chordá, M. Aquilué-Ballarí, I. Llagostera-Reverter, G. Mecho-Montoliu, A. Cervera-Gasch, et al. Developing and validating an integrated instrument for nursing assessments in adult hospitalization units: Study protocol Nurs Open., 10 (2023), pp. 4093-4100 https://doi.org/10.1002/nop2.1602
  • A. Mudd, R. Feo, T. Conroy, A. Kitson Where and how does fundamental care fit within seminal nursing theories: A narrative review and synthesis of key nursing concepts J Clin Nurs., 29 (2020), pp. 3652-3666 https://doi.org/10.1111/jocn.15420
  • L.C. Gray, E. Beattie, V.M. Boscart, A. Henderson, Y.C. Hornby-Turner, R.E. Hubbard, et al. Development and testing of the interRAI acute care: A standardized assessment administered by nurses for patients admitted to acute care Health Serv Insights., 11 (2018) 1178632918818836 https://doi.org/10.1177/1178632918818836
  • B. Oner, F.D. Zengul, N. Oner, N.V. Ivankova, A. Karadag, P.A. Patrician Nursing-sensitive indicators for nursing care: A systematic review (1997-2017) Nurs Open., 8 (2021), pp. 1005-1022 https://doi.org/10.1002/nop2.654
  • S. Bhandari, M.R. Hallowell Identifying and controlling biases in expert-opinion research: Guidelines for variations of delphi, nominal group technique, and focus groups J Manag Eng., 37 (2021), p. 04021015 https://doi.org/10.1061/(ASCE)ME.1943-5479.0000909
  • COSMIN. Guideline for Selecting Instruments for a Core Outcome Set [consultado 3 Ene 2024].
  • COSMIN. Checklists for Assessing Study Qualities [consultado 3 Ene 2024].
  • C. Douglas, C. Booker, R. Fox, C. Windsor, S. Osborne, G. Gardner Nursing physical assessment for patient safety in general wards: Reaching consensus on core skills J Clin Nurs., 25 (2016), pp. 1890-1900 https://doi.org/10.1111/jocn.13201
  • D. Luna-Aleixos, I. Llagostera-Reverter, X. Castelló-Benavent, M. Aquilué-Ballarín, G. Mecho-Montoliu, Á. Cervera-Gasch, et al. Development and validation of a meta-instrument for nursing assessment in adult hospitalization units (VALENF Instrument) (Part I) Int J Environ Res Public Health., 19 (2022), p. 14622 https://doi.org/10.3390/ijerph192214622
  • L.A. Slaughter, D. Soergel, T.C. Rindflesch Semantic representation of consumer questions and physician answers Int J Med Inform., 75 (2006), pp. 513-529 https://doi.org/10.1016/j.ijmedinf.2005.07.025
  • Tahvili S, Hatvani L, Felderer M, Afzal W, Saadatmand M, Bohlin M. Cluster-Based Test Scheduling Strategies Using Semantic Relationships between Test Specifications. 2018 IEEE/ACM 5th International Workshop on Requirements Engineering and Testing (RET), Gothenburg, Sweden, 2018, pp. 1-4
  • D. Luna-Aleixos, I. Llagostera-Reverter, X. Castelló-Benavent, M. Aquilué-Ballarín, G. Mecho-Montoliu, A. Cervera-Gasch, et al. Development and validation of a meta-instrument for the assessment of functional capacity, the risk of falls and pressure injuries in adult hospitalization units (VALENF Instrument) (Part II) Int J Environ Res Public Health., 20 (2023), p. 5003 https://doi.org/10.3390/ijerph20065003
  • E. Anthoine, L. Moret, A. Regnault, V. Sébille, J.B. Hardouin Sample size used to validate a scale: A review of publications on newly-developed patient reported outcomes measures Health Qual Life Outcomes., 12 (2014), p. 2 https://doi.org/10.1186/s12955-014-0176-2
  • M. White Sample size in quantitative instrument validation studies: A systematic review of articles published in Scopus, 2021 Heliyon., 8 (2022), p. e12223 https://doi.org/10.1016/j.heliyon.2022.e12223
  • M.J. Valero-Chillerón, I. Llagostera-Reverter, D. Luna-Aleixós, M. Moreno-Casbas, L. Andreu-Pejó, V.M. González-Chordá Explorando la validez de constructo del índice de Barthel en una muestra de pacientes hospitalizados españoles Enferm Clínica., 33 (2023), pp. 370-374 https://doi.org/10.1016/j.enfcli.2023.06.002
  • V.H.C. Botero, E.C. Cuervo, M.C. Bossio Acoso en el ambiente escolar: análisis de un cuestionario mediante Teoría de Respuesta al Ítem y Análisis de Correspondencias Múltiples Univ Psychol., 13 (2014), pp. 443-456 https://doi.org/10.1007/s11135-015-0218-9
  • A. Barth The changing nature of attitude constructs: An application of multiple correspondence analysis on gender role attitudes Qual Quant., 50 (2016), pp. 1507-1523 https://doi.org/10.1007/s11135-015-0218-9
  • C. Guinot, J. Latreille, D. Malvy, P. Preziosi, P. Galan, S. Hercberg, et al. Use of multiple correspondence analysis and cluster analysis to study dietary behaviour: Food consumption questionnaire in the SU.VI.MAX. cohort Eur J Epidemiol., 17 (2001), pp. 505-516 https://doi.org/10.1023/A:1014586129113
  • O.N. Medvedev, C.U. Krägeloh Rasch Measurement Model O.N. Medvedev, C.U. Krägeloh, R.J. Siegert, N.N. Singh (Eds.), Handbook of Assessment in Mindfulness Research, Cham, Springer (2022), pp. 1-18 https://doi.org/10.1007/978-3-030-77644-2_4-1
  • K.G.M. Moons, D.G. Altman, J.B. Reitsma, J.P.A. Ioannidis, P. Macaskill, E.W. Steyerberg, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and elaboration Ann Intern Med., 162 (2015), pp. W1-W73 https://doi.org/10.7326/M14-0698
  • M.C. Jansen, M.J. van der Oest, H.P. Slijper, J.T. Porsius, R.W. Selles Item reduction of the Boston Carpal Tunnel Questionnaire using decision tree modeling Arch Phys Med Rehabil., 100 (2019), pp. 2308-2313 https://doi.org/10.1016/j.apmr.2019.04.02
  • T. Das, S. Mobassirin, S.M.d.M. Hossain, A. Das, A. Sen, K.M.d.A. Kamal, et al. Patient questionnaires based Parkinson's disease classification using artificial neural network Ann Data Sci. (2023) https://doi.org/10.1007/s40745-023-00482-4
  • M. Morales-Puerto, M. Ruiz-Díaz, M. Aranda-Gallardo, J.M. Morales-Asencio, P. Alcalá-Gutiérrez, J.A. Rodríguez-Montalvo, et al. Development of a clinical prediction rule for adverse events in multimorbid patients in emergency and hospitalisation Int J Environ Res Public Health., 19 (2022), p. 8581 https://doi.org/10.3390/ijerph19148581
  • M. Ladios-Martin, M.J. Cabañero-Martínez, J. Fernández-de-Maya, F.J. Ballesta-López, A. Belso-Garzas, F.M. Zamora-Aznar, et al. Development of a predictive inpatient falls risk model using machine learning J Nurs Manag., 30 (2022), pp. 3777-3786 https://doi.org/10.1111/jonm.13760
  • Z. Huang, B. Sun, S. Wu, X. Meng, Y. Cong, G. Shen, et al. A nomogram for predicting survival in patients with breast cancer brain metastasis Int J Radiat Oncol Biol Phys., 99 (2017), p. E19 https://doi.org/10.1016/j.ijrobp.2017.06.634
  • D.G. Altman, P.M.M. Bossuyt Estudios de precisión diagnóstica (STARD) y pronóstica (REMARK) Med Clínica., 125 (2005), pp. 49-55 https://doi.org/10.1016/S0025-7753(05)72210-7
  • V.M. González-Chordá, D.L. Aleixos, I.L. Reverter, A. Cervera-Gash, F.H. Machancoses, M.T. Moreno-Casbas, et al. Diagnostic accuracy study of the VALENF instrument in hospitalization units for adults: A study protocol BMC Nurs., 22 (2023), p. 401 https://doi.org/10.1186/s12912-023-01567-4
  • V.M. González-Chordá Enfermería de precisión y cuidados personalizados Enferm Clínica., 34 (2024), pp. 1-3 https://doi.org/10.1016/j.enfcli.2023.11.003
  • N. Wang, L. Lv, F. Yan, Y. Ma, L. Miao, L.Y. Foon Chung, et al. Biomarkers for the early detection of pressure injury: A systematic review and meta-analysis J Tissue Viability., 31 (2022), pp. 259-267 https://doi.org/10.1016/j.jtv.2022.02.005
La source de données: Dialnet