Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2019

Open Access 01-12-2019 | Care | Research article

Assessing the maturity of the healthcare system for integrated care: testing measurement properties of the SCIROCCO tool

Authors: Liset Grooten, Hubertus Johannes Maria Vrijhoef, Stefano Calciolari, Laura Guadalupe González Ortiz, Marcela Janečková, Mirella M. N. Minkman, Dirk Devroey

Published in: BMC Medical Research Methodology | Issue 1/2019

Login to get access

Abstract

Background

The Scaling Integrated Care in Context (SCIROCCO) tool has been developed to facilitate knowledge transfer and learning about the implementation and scaling-up of integrated care in European regions. To adequately test the functionality of the tool in assessing the maturity for integrated care within regions, this study evaluated its structural validity, internal consistency and convergent validity.

Methods

Exploratory factor analysis was used to investigate the structural validity of the 12-items of the SCIROCCO tool. Hereafter, the internal consistency was assessed by calculating Cronbach’s and ordinal alpha. The convergent validity was explored by testing 23 pre-hypothesized relationships between items of the SCIROCCO tool and items of an instrument measuring a similar construct.

Results

Factor analysis revealed a one-factor structure. Cronbach’s alpha of the overall instrument was 0.92, ordinal alpha was 0.94. Only 30.34% of the hypotheses for testing the convergent validity were met.

Conclusion

The one-factor structure is considered relevant in representing the structural validity of the SCIROCCO tool. The scale of the SCIROCCO tool shows good internal consistency. The tool (DMIC Quickscan) used to assess the convergent validity might measure a different aspect of integrated care than the SCIROCCO tool. Further research is needed to continue investigating the validity and reliability of the tool.
Appendix
Available only for authorised users
Literature
1.
Henderson D, Pavlickova A, Lewis L. Scalability and transferability of good practices in Europe: what does it take? Int J Integr Care. 2016;16:1–2.CrossRef
2.
Mangham LJ, Hanson K. Scaling up in international health: what are the key issues? Health Policy Plan. 2010;25:85–96.CrossRef
3.
Grooten L, Alexandru C-A, Alhambra-Borrás T, Anderson S, Avolio F, Valia Cotanda E, et al. Journal of integrated care a scaling-up strategy supporting the expansion of integrated care: a study protocol article information. 2018; Available from: https://​doi.​org/​10.​1108/​JICA-04-2018-0029.
4.
de Vet HCW, Terwee CB, Mokkink LB, Knol DJ. Measurement in Medicine: A Practical Guide. New York: Cambridge University Press; 2011.
5.
Grooten L, Vrijhoef HJM, Borgermans L. An instrument to measure maturity of integrated care: a first validation study. Int J Integr Care. 2018;18(1):10.CrossRef
6.
Mokkink LB, Terwee CB, Knol DL, Stratford PW, Alonso J, Patrick DL, et al. The COSMIN checklist for evaluating the methodological quality of studies on measurement properties: a clarification of its content. BMC Med Res Methodol. 2010;10:22.CrossRef
7.
Carlson KD, Herdman AO. Understanding the impact of convergent validity on research results. Organ Res Methods. 2012;15:17–32.CrossRef
8.
Minkman M, Ahaus K, Fabbricotti I, Nabitz U, Huijsman R. A quality management model for integrated care: results of a Delphi and concept mapping study. Int J Qual Heal Care. 2009;21:66–75.CrossRef
9.
10.
Minkman MMN, Vermeulen RP, Ahaus KTB, Huijsman R. The implementation of integrated care: the empirical validation of the development model for integrated care. BMC Health Serv Res. 2011;11:177.CrossRef
11.
Zonneveld N, Vat LE, Vlek H, Minkman MMN. The development of integrated diabetes care in the Netherlands: a multiplayer self-assessment analysis. BMC Health Serv Res. BMC Health Serv Res. 2017;17:1–9.CrossRef
12.
Vat LE, Middelkoop I, Buijck BI, Minkman MMN. The development of integrated stroke Care in the Netherlands a benchmark study. Int J Integr Care. 2016;16:12.CrossRef
13.
Longpré C, Dubois C-A. Implementation of integrated services networks in Quebec and nursing practice transformation: convergence or divergence? BMC Health Serv Res. 2015;15:84.CrossRef
14.
Courtney MGR. Determining the number of factors to retain in EFA : using the SPSS R-menu v2 . 0 to make more judicious estimations. Pract Assessment Res Eval. 2013;18:1–14.
15.
Holgado-Tello FP, Chacón-Moscoso S, Barbero-García I, Vila-Abad E. Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Qual Quant. 2009;44:153–66.CrossRef
16.
Jöreskog KG. Factor analysis by MINRES. Chicago Sci Softw Retrieved June. 2003;1:1–13.
17.
Horn JL. A rationale and test for the number of factors in factor analysis. Psychometrika. 1965;30:179–85.CrossRef
18.
Ruscio J, Roche B. Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychol Assess. 2012;24:282–92.CrossRef
19.
Garrido LE, Abad FJ, Ponsoda V. A new look at horn’s parallel analysis with ordinal variables. Psychol Methods. 2013;18:454–74.CrossRef
20.
Costello AB, Osborne JW. Best practices in exploratory factor analysis : four recommendations for getting the Most from your analysis. Pract Assessment Res Educ. 2005;10:1–9.CrossRef
21.
Gaskin CJ, Happell B. On exploratory factor analysis: a review of recent evidence, an assessment of current practice, and recommendations for future use. Int J Nurs Stud. 2014;51:511–21.CrossRef
22.
Basto M, Pereira JM. An SPSS R -menu for ordinal factor analysis. J Stat Softw. 2012;46:1–29.CrossRef
23.
Thompson B. Exploratory and confirmatory factor analysis: understanding concepts and applications. Washington, DC: American Psychological Association; 2004. p. 2004.CrossRef
24.
Zumbo BD, Gadermann AM, Zeisser C. Ordinal versions of coefficients alpha and Theta for Likert rating scales. J Mod Appl Stat Methods. 2007;6:21–9.CrossRef
25.
Chalmers RP. On misconceptions and the limited usefulness of ordinal alpha. Educ Psychol Meas. 2017;78(6):1056–71.
26.
Hinkle DE, Wiersma W. Applied Statistics for the behavioral sciences. Boston: Houghton Mifflin; 2002.
27.
MacKinnon DP, Lockwood CM, Williams J. Confidence limits for the indirect effect: distribution of the product and resampling methods. Multivariate Behav Res. 2004;39:99–128.CrossRef
28.
Chan W, Chan DW-L. Bootstrap standard error and confidence intervals for the correlation corrected for range restriction: a simulation study. Psychol Methods. 2004;9:369–85.CrossRef
29.
Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New York, NY Chapman & Hall. 1993.
30.
Sapnas KG, Zeller RA. Minimizing sample size when using exploratory factor analysis for measurement. J Nurs Manag. 2002;10:135–54.
31.
de Winter JCF, Dodou D, Wieringa PA. Exploratory factor analysis with small sample sizes. Multivariate Behav Res. 2009;44:147–81.CrossRef
32.
Jung S. Exploratory factor analysis with small sample sizes: a comparison of three approaches. Behav Process. 2013;97:90–5.CrossRef
Metadata
Title
Assessing the maturity of the healthcare system for integrated care: testing measurement properties of the SCIROCCO tool
Authors
Liset Grooten
Hubertus Johannes Maria Vrijhoef
Stefano Calciolari
Laura Guadalupe González Ortiz
Marcela Janečková
Mirella M. N. Minkman
Dirk Devroey
Publication date
01-12-2019
Publisher
BioMed Central
Keyword
Care
Published in
BMC Medical Research Methodology / Issue 1/2019
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-019-0704-1

Other articles of this Issue 1/2019

BMC Medical Research Methodology 1/2019 Go to the issue

Research article

Mixing mixed-mode designs in a national health interview survey: a pilot study to assess the impact on the self-administered questionnaire non-response

Research article

Privacy-protecting estimation of adjusted risk ratios using modified Poisson regression in multi-center studies

Research article

A methodological systematic review of meta-ethnography conduct to articulate the complex analytical phases

Research article

Comparison of reproductive history gathered by interview and by vital records linkage after 40 years of follow-up: Bogalusa Babies

Research article

Effect of prepaid and promised financial incentive on follow-up survey response in cigarette smokers: a randomized controlled trial

Research article

Evaluating selection bias in a population-based cohort study with low baseline participation: the LIFE-Adult-Study