# Healthcare Students' Ethical Considerations of Care Robots in The Netherlands
## 2 Methods and Materials
### Measures  
Each construct was measured by Likert-type items, balanced for indicative and counter-indicative items, each followed by six-point rating scales (1 = strongly disagree, 6 = strongly agree), avoiding the neutral position [32]. Items were recoded afterwards to all point into the same direction as indicated here. Measurement scales were optimized based on principal component analysis (PCA) and reliability analyses (SPSS-V21). PCA was run with direct Oblimin rotation to create uncorrelated factors for the questionnaire items ([PERSON], 2009, p. 671). The final analysis comprised four factors explaining 65.09% of the variance including 18 items with primary loadings over 0.5 and no cross-loadings larger than 0.3. Composite scores were created for each of the four factors based on the mean of the items. The internal consistency of the resulting scales was well beyond the minimum with Cronbach alpha's > 0.70 [33] (see Table 2). Below, we describe how we measured each of the ethical categories.  
Autonomy (3 items; \(\alpha\) = 0.87) was defined as a person's ability to make his/her own choices independently without the interference of other people or outsiders [20]. Example items are: \"A care robot increases the independence of a patient\" (indicative) and \"A care robot makes the patient more dependent\" (counter-indicative).  
Beneficence referred to a person's sense of health and well-being [20]. A sample item is: \"A care robot treats the patient well.\" Non-maleficence refers to a device or treatment not doing harm to patients [20]. Because care professionals actually think in terms of maleficence, we omitted the equivocal \"non\" from the original scale label. Whereas \"maleficence\" is not exactly the opposite of non-maleficence, this phrasing was more comprehensible. A sample item is: \"A care robot is an obstacle for improving health.\" However, results of scale optimization showed a better fit for using only one scale to cover both Benefice and Maleficence (4 items; \(\alpha\) = 0.85). The dominant items on that scale indicted the name Maleficence.  
Justice pertained to the need to distribute care evenly across patients [20]. An example item is: \"A care robot keeps its promises\". However, the items did not form a reliable scale and we therefore dismissed it from further analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **\# Items** & **Mean** & **SD** & **Cronbach \(\alpha\)** \\ \hline Maleficence & 4 & 3.22 & 0.95 & 0.85 \\ Autonomy & 3 & 3.5 & 1.10 & 0.87 \\ Utility & 5 & 4.17 & 0.82 & 0.78 \\ Acceptance & 6 & 3.99 & 0.99 & 0.87 \\ \hline \hline \end{tabular} Note.: Items were scored on six-point rating scales (1 = strongly disagree, 6 = strongly agree).  
\end{table}
Table 2: Descriptive statistics of the four composite factors used for further statistical analyses (\(N\) = 357).  
Utility (five items; \(\alpha\) = 0.78) concerned the degree to which care professionals believed a robot is practical, \"handy,\" and useful during the execution of their jobs [24]. A sample item is: \"A care robot is practical\".  
Acceptance (six items; \(\alpha\) = 0.87) referred to care professionals giving admittance and approval to a care robot without protest or refutation, while regarding a robot as a proper and normal care utensil [25]. A sample item is: \"I will accept the assistance of a care robot\".  
An approximately normal distribution was evident for the composite score data in the current study, with skewness and kurtosis scores between \(-1\) and \(1\), thus making the data well-suited for parametric statistical analyses.