Abstract

Objective: Evaluating physicians’ attitudes towards malnutrition and clinical nutrition in hospitalized patients are crucial for the implementation of optimal nutritional care process and the prevent of hospital malnutrition. The aim of this study is to develop a scale that evaluates physicians’ attitudes towards malnutrition in hospitalized patients.

Methods: Based on the existing literature on clinical nutrition and the clinical experience of experts in this field, a 5-point Likert-type attitude scale consisting of 12 items was developed. Analysis was carried out using Parallel Analysis to determine the number of factors in the Exploratory factor analysis based on the Polychoric correlation matrix and Unweighted Least Squares as the factor extraction method.

Results: There are 8 items in the 1st factor (Physician duties) and 4 items in the 2nd factor (Non-Physician duties). The Cronbach Alpha and McDonald’s Omega coefficients of the scale were found to be 0.72 and 0.81 respectively, from the sub-dimensions 0.78 and 0.85 for the 1st Factor, and 0.66 and 0.75 for the 2nd Factor.

Conclusion: Attitude scale for the clinical nutrition care process of hospitalized patients for physicians is an instrument with good psychometric properties that measures examination of physicians’ attitudes related to clinical nutrition care process.

Keywords: attitude scale, clinical nutrition care, physicians

Copyright and license

How to cite

1.
Ulusoy H, Delibalta B, Kangalgil M, Kumlu G, Kaynar K, Nuhoğlu İ. Development and validation of the attitude scale for the clinical nutrition care process of hospitalized patients for physicians. Clin Sci Nutr. 2024;6(2):80-87. doi:10.62210/ClinSciNutr.2024.88

References

  1. Sauer AC, Goates S, Malone A, et al. Prevalence of malnutrition risk and the impact of nutrition risk on hospital outcomes: Results from nutrition day in the U.S. JPEN J Parenter Enteral Nutr. 2019;43:918-926. https://doi.org/10.1002/jpen.1499
  2. Lew CCH, Wong GJY, Cheung KP, Chua AP, Chong MFF, Miller M. Association between malnutrition and 28-day mortality and intensive care length-of-stay in the critically ill: A prospective cohort study. Nutrients. 2017;10:10. https://doi.org/10.3390/nu10010010
  3. Marinho R, Pessoa A, Lopes M, et al. High prevalence of malnutrition in Internal Medicine wards - a multicentre ANUMEDI study. Eur J Intern Med. 2020;76:82-88. https://doi.org/10.1016/j.ejim.2020.02.031
  4. Lengfelder L, Mahlke S, Moore L, Zhang X, Williams G, Lee J. Prevalence and impact of malnutrition on length of stay, readmission, and discharge destination. JPEN J Parenter Enteral Nutr. 2022;46:1335-1342. https://doi.org/10.1002/jpen.2322
  5. van Vliet IM, Gomes-Neto AW, de Jong MF, Jager-Wittenaar H, Navis GJ. High prevalence of malnutrition both on hospital admission and predischarge. Nutrition. 2020;77:110814. https://doi.org/10.1016/j.nut.2020.110814
  6. Bargetzi L, Brack C, Herrmann J, et al. Nutritional support during the hospital stay reduces mortality in patients with different types of cancers: secondary analysis of a prospective randomized trial. Ann Oncol. 2021;32:1025-1033. https://doi.org/10.1016/j.annonc.2021.05.793
  7. Kaegi-Braun N, Mueller M, Schuetz P, Mueller B, Kutz A. Evaluation of nutritional support and in-hospital mortality in patients with malnutrition. JAMA Netw Open. 2021;4:e2033433. https://doi.org/10.1001/jamanetworkopen.2020.33433
  8. Correia MIT, Sulo S, Brunton C, et al. Prevalence of malnutrition risk and its association with mortality: nutritionDay Latin America survey results. Clin Nutr. 2021;40:5114-5121. https://doi.org/10.1016/j.clnu.2021.07.023
  9. Reber E, Strahm R, Bally L, Schuetz P, Stanga Z. Efficacy and efficiency of nutritional support teams. J Clin Med. 2019;8:1281. https://doi.org/10.3390/jcm8091281
  10. Jensen GL, Compher C, Sullivan DH, Mullin GE. Recognizing malnutrition in adults: definitions and characteristics, screening, assessment, and team approach. JPEN J Parenter Enteral Nutr. 2013;37:802-807. https://doi.org/10.1177/0148607113492338
  11. Duerksen DR, Keller HH, Vesnaver E, et al. Physicians' perceptions regarding the detection and management of malnutrition in Canadian hospitals: Results of a Canadian Malnutrition Task Force survey. JPEN J Parenter Enteral Nutr. 2015;39:410-417. https://doi.org/10.1177/0148607114534731
  12. Karim SA, Ibrahim B, Tangiisuran B, Davies JG. What do healthcare providers know about nutrition support? A survey of the knowledge, attitudes, and practice of pharmacists and doctors toward nutrition support in Malaysia. JPEN J Parenter Enteral Nutr. 2015;39:482-488. https://doi.org/10.1177/0148607114525209
  13. Grammatikopoulou MG, Katsouda A, Lekka K, et al. Is continuing medical education sufficient? Assessing the clinical nutrition knowledge of medical doctors. Nutrition. 2019;57:69-73. https://doi.org/10.1016/j.nut.2018.05.013
  14. Kirbiyik F, Ozkan E. Knowledge and practices of medical oncologists concerning nutrition therapy: A survey study. Clin Nutr ESPEN. 2018;27:32-37. https://doi.org/10.1016/j.clnesp.2018.07.004
  15. Han SL, Auer R, Cornuz J, Marques-Vidal P. Clinical nutrition in primary care: An evaluation of resident physicians' attitudes and self-perceived proficiency. Clin Nutr ESPEN. 2016;15:69-74. https://doi.org/10.1016/j.clnesp.2016.06.005
  16. McGaghie WC, Van Horn L, Fitzgibbon M, et al. Development of a measure of attitude toward nutrition in patient care. Am J Prev Med. 2001;20:15-20. https://doi.org/10.1016/s0749-3797(00)00264-6
  17. Finney SJ, DiStefano C. Nonnormal and categorical data in structural equation modeling. In: Hancock GR, Mueller RO, editors. Structural equation modeling: A second course. 3rd ed. Charlotte NC: IAP; 2013: 439-492.
  18. Tabachnick BG, Fidell LS. Using multivariate statistics. 6th ed. Boston: Pearson; 2012.
  19. Velicer WF, Jackson DN. Component analysis versus common factor analysis: Some issues in selecting an appropriate procedure. Multivariate Behav Res. 1990;25:1-28. https://doi.org/10.1207/s15327906mbr2501_1
  20. Jung S. Exploratory factor analysis with small sample sizes: A comparison of three approaches. Behav Processes. 2013;97:90-95. https://doi.org/10.1016/j.beproc.2012.11.016
  21. Kline RB. Principles and practise of structural equating modeling. 3th ed. The Guilford Press; 2011.
  22. Kılıç AF. Exploratory factor analysis with R software. Anadolu University Journal of Education Faculty. 2020;4:276-293.
  23. Şencan H, Fidan Y. Normality assumption in the exploratory factor analysis with likert scale data and testing its effect on factor extraction. BMIJ. 2020;8:640-687. https://doi.org/10.15295/bmij.v8i1.1395
  24. van der Eijk C, Rose J. Risky business: factor analysis of survey data - assessing the probability of incorrect dimensionalisation. PLoS One. 2015;10:e0118900. https://doi.org/10.1371/journal.pone.0118900
  25. Kalaycı Ş. Faktör analizi. In: Kalaycı Ş, editor. SPSS uygulamalı çok değişkenli istatistik teknikleri. Ankara: Asil Yayın Dağıtım; 2014: 321-331.
  26. Lloret S, Ferreres A, Hernández A, et al. The exploratory factor analysis of items: Guided analysis based on empirical data and software. Anales de Psicología. 2017;33(2):417-432. https://doi.org/10.6018/analesps.33.2.270211
  27. Bryman A, Cramer D. Quantitative data analysis with SPSS release 8 for Windows. Routledge: Taylor and Francis e-Librar; 1999.
  28. Kline P. An easy guide to factor analysis. London: Routledge; 1994.
  29. Çokluk Ö, Şekercioğlu G, Büyüköztürk Ş. Sosyal bilimler için çok değişkenli istatistik. Ankara: Pegem Akademi Yayınları; 2010.
  30. Revelle WR. psych: Procedures for Personality and Psychological Research 2017.
  31. Kelley TL. Essential traits of mental life, Harvard studies in education. Cambridge: Harvard University Press; 1935.
  32. Schermelleh-Engel K, Moosbrugger H, Müller H. Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online. 2003;8(2):23-74.
  33. Yu C, Muthen B. Evaluation of model fit indices for latent variable models with categorical and continuous outcomes. In: Annual Meeting of the American Educational Research Association. New Orleans, LA; 2002.