Understanding social dynamics and patient experience in out of hospital care: validation of a co-responsibility questionnaire (CoReCare)

Abstract

Patient experiences are commonly assessed through patient reported experience measures (PREMs). Ambulatory care models extend traditional care into the patients' home, meaning that a triangle of health care professionals, patients, and their families need to be considered when assessing the remote care experience. These intertwined responsibilities are described by co-responsibility. Currently, PREMs don't reflect how elements to remote care impact this remote care experience. Therefore, this study aimed to develop a questionnaire assessing perceived patient-partner co-responsibility as a PREM in remote care. A 30-item questionnaire was assessed among 1000 individuals aged between 18 and 65 years that tried to lose weight with a partner, friend or family member supporting them. Pairwise item correlations, Exploratory Factor Analysis, and Cronbach's alpha were used for validation. 29-items were identified to reflect co-responsibility across 6 factors: empowerment and support, relational aspects, lack of sympathy, co-participation, accepting help and awareness. Cronbach's alpha ranged between 0.66 and 0.93, showing good internal consistency. We present a validated CoReCare Questionnaire to understand the impact of social dynamics on achieving desired health outcomes in a remote care setting. The CoReCare Questionnaire extends current PREMs when aiming to assess and improve the patient experience of a care episode outside of the hospital.

Figure 1

Correlation matrix for the questionnaire items (after inversion of negatively phrased questions). Codes are mapped to items in the first column of Table 2. Since items are sorted into groups of similar correlations, it is already possible to see some of the structures that are identified by factor analysis, e.g. I12, I13, and I14 (Factor 5), or I17, I26, and I27 (contributing to Factor 3).

Figure 2

Scree plot of factor eigenvalues for a factor analysis of the co-responsibility items with no rotation. The choice of 6 factors satisfies both Kaiser criterion (the solid horizontal line is located at y = 1) and Cattell criterion (the so-called elbow method).

Figure 3

Pairwise relationships between the identified constructs. The plots on the diagonal show the histogram of the total score; in the lower triangle are pairwise binned scatterplots with linear fit; in the upper triangle, pairwise Pearson’s correlation coefficients. The score of each construct is normalized between 0 and 1 to allow easier comparison. Binned scatterplots are drawn by dividing datapoints on the x-coordinate in 10 groups of equal size and computing the mean of the y-coordinate. Pearson’s $\rho$ coefficients correspond to the plot that is symmetric with respect to the main diagonal, and they are directly linked to the R² coefficient of the corresponding linear fit.