Humanisation of care pathways: training program evaluation among healthcare professionals


Abstract


In recent years, medical care has come a long way thanks to technological improvements in diagnostics and treatment. Healthcare providers have developed many new care pathways, improving the organisation of care processes. In this context, the care pathway reduces the patient’s choices, and the relationship between the health professional and the patient is less personal; the main drawback of this is that it could lead to dehumanisation of the patient. In 2015, the Local Health Unit of Naples (ASL NA1) set up the “Humanisation” project with the aim of accepting the patient as unique – as a human being – in care pathways. In this paper, we analyse the features of health professionals’ satisfaction related to various aspects of a training course for health workers involved in the Humanisation project. We use a recent implementation of classification trees for ordered categorical response variables in order to identify the most relevant determinants of satisfaction. The results show that the main determinants of participants’ satisfaction are the professional competence and responsiveness of the teachers, the skills acquired in the training course and increased personal awareness as a perceived outcome. Implications for the implementation of the Humanisation project are discussed.


Keywords: Humanisation, Tree-based methods, Ordinal response, Cure pathway, Healthcare professionals, Student satisfaction.

References


Barrios Casas, S. and Paravic Kliin, T. (2009). Percepciòn de violencia de usuarios hospitalizados en los servicios cl ́ınicos de un hospital pu ́blico de la regiòn de la arau- canìa-chile. Ciencia y enfermerìa, 15(3):29–43.

Breiman, L., Friedman, J., Olshen, R. A., and Stone, C. J. (1984). Classification and regression trees. CRC press.

Carpita, M. and Vezzoli, M. (2012). Statistical evidence of the subjective work quality: the fairness drivers of the job satisfaction. Electronic Journal of Applied Statistical Analysis, 5(1):89–107.

Ceballos Vasquez, P. A. (2010). Desde los àmbitos de enfermerìa, analizando el cuidado humanizado. Ciencia y enfermerìa, 16(1):31–35.

Chipman, H., George, E., and McCulloch, R. (1998). Bayesian cart model search. journal of the american statistical association.

D’Ambrosio, A. and Heiser, W. J. (2016). A recursive partitioning method for the predic- tion of preference rankings based upon kemeny distances. Psychometrika, 81(3):774– 794.

Douglas, J., Douglas, A., and Barnes, B. (2006). Measuring student satisfaction at a uk university. Quality assurance in education, 14(3):251–267.

Elliott, K. M. and Healy, M. A. (2001). Key factors influencing student satisfaction re- lated to recruitment and retention. Journal of marketing for higher education, 10(4):1– 11.

Elliott, K. M. and Shin, D. (2002). Student satisfaction: An alternative approach to as- sessing this important concept. Journal of Higher Education Policy and Management, 24(2):197–209.

Fontaine, M. (2014). Student relationship management (srm) in higher education: Ad- dressing the expectations of an ever evolving demographic and its impact on retention. Journal of Education and Human Development, 3(2):105–119.

Fujita, N., Perrin, X. R., Vodounon, J. A., Gozo, M. K., Matsumoto, Y., Uchida, S., and Sugiura, Y. (2012). Humanised care and a change in practice in a hospital in benin. Midwifery, 28(4):481–488.

Galimberti, G., Soffritti, G., Maso, M. D., et al. (2012). Classification trees for ordinal responses in r: the rpartscore package. Journal of Statistical Software, 47(i10).

Gonzàlez-Hernàndez, O. J. (2015). Validez y confi abilidad del instrumento” percepci ́on de comportamientos de cuidado humanizado de enfermerìa pche 3a versiòn”. Aquichan, 15(3):381–392.

Gregg, W. E. (1972). Several factors affecting graduate student satisfaction. The Journal of Higher Education, 43(6):483–498.

Grunwald, H. and Peterson, M. W. (2003). Factors that promote faculty involvement in and satisfaction with institutional and classroom student assessment. Research in Higher Education, 44(2):173–204.

Guillén Velasco, R. (2010). La connotaciòn humana y cultural del cuidado. Biblioteca las Casas, 6(3).

Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning, ser. Springer Series in Statistics (Second Edition). New York, NY, USA: Springer New York Inc.

Hill, F. M. (1995). Managing service quality in higher education: the role of the student as primary consumer. Quality assurance in education, 3(3):10–21.

Iorio, C., Aria, M., and D’Ambrosio, A. (2015). A new proposal for tree model selection and visualization. In Advances in Statistical Models for Data Analysis, pages 149–156. Springer.

Letcher, D. W. and Neves, J. S. (2010). Determinants of undergraduate business student satisfaction. Research in Higher Education Journal, 6:1.

Martins, J. d. J., Backes, D. S., Cardoso, R. d. S., Erdmann, A. L., and Albuquerque, G. L. d. (2008). Resignificando la humanizaciòn desde el cuidado en el curso de vivir humano. Rev. enferm. UERJ, 16(2):276–281.

Montanari, A. and Monari, P. (2008). Gini s ideas: new perspectives for modern multivariate statistical analysis. Statistica, 68(3/4):239–254.

Moro-Egido, A. I. and Panades, J. (2010). An analysis of student satisfaction: Full-time vs. part-time students. Social Indicators Research, 96(2):363–378.

Patton, M. Q. (1997). Utilization-focused evaluation: the new century text.

Petruzzellis, L., D’Uggento, A. M., and Romanazzi, S. (2006). Student satisfaction and quality of service in italian universities. Managing Service Quality: An International Journal, 16(4):349–364.

Piccarreta, R. (2008). Classification trees for ordinal variables. Computational Statistics, 23(3):407–427.

R Core, T. (2016). R: A language and environment for statistical computing. vienna: R foundation for statistical computing. available online at: http. www.R-project.org.

Romero-Massa, E., Contreras-Méndez, I., Pérez-Pàjaro, Y., Moncada, A., and Jiménez- Zamora, V. (2013). Cuidado humanizado de enfermerìa en pacientes hospitalizados. cartagena, colombia. Revista Ciencias Biomédicas, 4(1).

Siciliano, R., Tutore, V. A., Aria, M., and D’Ambrosio, A. (2010). Trees with leaves and without leaves. In 45th scientific meeting of the Italian Statistical Society. Italian Statistical Society.

Sinclaire, J. K. (2014). An empirical investigation of student satisfaction with college courses. Research in Higher Education Journal, 22:1.

Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinfor- matics, 8(1):25.

Sweeney, J. C. and Ingram, D. (2001). A comparison of traditional and web-based tuto- rials in marketing education: An exploratory study. Journal of Marketing Education, 23(1):55–62.

Therneau, T. M., Atkinson, E. J., et al. (1997). An introduction to recursive partitioning using the rpart routines. Technical report, Technical report Mayo Foundation.

Yorke, M. (1999). Assuring quality and standards in globalised higher education. Quality Assurance in Education, 7(1):14–24.


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