Background
The global nurse shortage, coupled with a rise in nursing student dropout rates, has profound implications for individuals, families, educational institutions, and society at large [1, 2]. While challenges within nursing programs contribute to student attrition [3, 4], a significant factor is the lack of interest in the nursing profession [5, 6]. Study interest, a vital motivational factor [7], influences students’ engagement with content and their capacity for deep learning [8]. Research indicates that study interest positively impacts nursing students’ academic performance [9], self-efficacy [10], critical thinking [11], and learning satisfaction [12]. Consequently, study interest plays a crucial role in improving nursing students’ learning state and quality, and the development of study interest is a powerful tool for fostering deeper learning [7].
Many researchers have explored methods to enhance study interest among nursing students [13‐15]. However, there is currently a lack of dedicated measurement tools for assessing nursing students’ study interest in China [16]. Existing measures mainly focus on assessing the interest of primary and secondary school students in specific subjects [16‐18]. While scales are available to measure study interest among college students, they are often tailored to specific subjects or situations [19, 20]. Consequently, it is essential to utilize a measurement tool specifically developed to evaluate college students’ interest in professional learning to obtain accurate results. Employing an inappropriate scale may result in an inability to accurately capture the intended construct, potentially introducing systematic measurement errors and biasing study outcomes [21]. Therefore, selecting an appropriate scale for assessing study interest is critical for minimizing measurement bias and enhancing the overall quality of research findings.
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The Study Interest Questionnaire (SIQ) has been identified as a suitable tool for assessing college students’ interest in professional learning [22]. Grounded in educational interest theory, the original SIQ consists of 18 items [22]. It has been translated into multiple languages and widely used [11, 23]. In 2013, a scholar proposed a condensed version of the SIQ known as the SIQ-SF [24]. The SIQ-SF exhibits robust reliability and validity [24], with the added benefit of containing only 9 items, thereby saving responders valuable time. Considering the good reliability, validity, and widespread applicability of the SIQ-SF [24‐26], the objective of this study was to validate its usage among Chinese nursing students. To better evaluate the psychological characteristics of the Chinese version of the SIQ-SF, classical test theory (CTT) and item response theory (IRT) were employed.
Methods
This cross-sectional study was conducted in two distinct phases: (1) translation and cross-cultural adaptation of the SIQ-SF and (2) validation of the SIQ-SF in a sample of Chinese nursing students.
The nine items of the SIQ-SF capture three factors of study interest: emotion (5 items), value (2 items), and intrinsic quality (2 items) [22]. Each item is rated on a Likert scale from 1 (not at all true) to 4 (completely true), with items 2 and 5 scored in reverse [24]. The total score, which ranges from 9 to 36, is calculated by summing the scores of all nine items [24]. A higher total score indicates a greater level of interest in the student’s professional learning [24]. The SIQ-SF demonstrates satisfactory reliability (Cronbach’s α = 0.82) [22] and validity, effectively measuring students’ interest in professional learning [24‐26].
Phase 1: translation and cross-cultural adaptation
To maintain semantic consistency with the SIQ-SF, the translation and cross-cultural adaptation rigorously followed the modified Brislin model [27]. The process included the following four steps: (1) Forward translation: The SIQ-SF was independently translated from English to Chinese by two bilingual translators residing in China, both of whom have nursing doctorates. Subsequently, a panel comprising five nursing educators, two bilingual translators and one English lecturer deliberated and modified two versions of the translated SIQ-SF together, ultimately reaching a consensus on the phrasing and expression of an initial Chinese version of the SIQ-SF. (2) Backward translation: Two additional bilingual translators who worked in the UK translated the initial Chinese version of the SIQ-SF back into English and were blinded to the original SIQ-SF. These two bilingual translators both hold master’s degrees. Then, the same expert panel and the two bilingual translators working in the UK undertook the task of scrutinizing the back-translation in comparison to the original text. The objective of this comparison was to identify and rectify any linguistic errors present in both translations and finally form the draft Chinese version of the SIQ-SF that best matches the original English version. (3) Cross-Cultural Adaptation: A group of ten experts were invited to enhance the draft Chinese version of the SIQ-SF by aligning it with Chinese expressions and habits, drawing upon their professional expertise and work experience. Each of these experts holds a doctorate, and they specialize in various fields, including nursing, education, medicine, and psychology. The translational relevance of each of the 9 items was graded on a 4-point scale (1 = “totally different” and 4 = “equivalent”). (4) Pilot study: Ten nursing students were invited to evaluate the fluency, readability, and comprehensibility of the items. Any inappropriacy was modified according to the received feedback, and the final Chinese version of the SIQ-SF was obtained.
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Phase 2: validation
Participants and settings
Convenience sampling was employed in the present study. Participants who fulfilled the following criteria were included: (1) Chinese nursing student, (2) over 18 years old, and (3) volunteering to participate. Participants who demonstrated an inability to comprehend and produce written Chinese were excluded. To optimize the sample selection process, three provinces representing the eastern, central, and western regions of mainland China were chosen, considering variations in economic development levels and educational resources. Additionally, recognizing the distinct educational approaches in the two special administrative regions (SAR) of China (Hong Kong and Macao), compared to other provinces in mainland China, it is necessary to include some samples from SAR. Therefore, samples were ultimately collected from the eastern province of Jiangsu, the central province of Hunan, the western province of Sichuan, and the Macao SAR. Ideally, the sample size should consist of at least 500 participants for the factor analysis in CTT [28]. Although the appropriate sample size for the Rasch Rating Scale Model (RRSM) in IRT has not been determined with certainty, for the purpose of estimating fit statistics, a sample of 500 students was considered adequate [28].
Procedures
The data were collected using Wenjuanxing (https://www.wjx.cn/), a well-known Chinese online questionnaire survey platform. Initially, the research team generated promotional materials, such as posters incorporating QR codes for electronic questionnaires. Subsequently, the research coordinators in each province assumed the responsibility of disseminating the research through social media platforms within their respective regions. To ensure the completion of the questionnaire, participants were required to respond to all questions prior to submission. Furthermore, each participant was limited to a single instance of answering the questionnaire, as determined by the IP address documented by the Wenjuanxing survey platform.
Statistical analysis
To conduct the analysis, the total sample was randomly divided into two equal-sized groups. The randomization techniques help to distribute any potential confounding variables evenly across groups, thus reducing demographic differences [29]. The first group of samples was used for exploratory factor analysis (EFA), and the second group of samples was used for confirmatory factor analysis (CFA) and Rasch analysis (RA). The EFA was conducted with SPSS 29.0, the CFA was conducted with AMOS 22.0, and the RA was conducted with Winsteps 3.74.0. Demographic characteristics (such as age, sex, and education) were analyzed through descriptive statistics. A p value < 0.05 was considered to indicate statistical significance [30].
CTT analysis
The validity of the Chinese version of the SIQ-SF was assessed through content validity and construct validity. The Content Validity Index (CVI) was calculated by initially categorizing scores of 3 and 4 as indicating ‘relevance’ (assigned a value of 1) and scores of 1 and 2 as indicating ‘irrelevance’ (assigned a value of 0). The item content validity index (I-CVI) and scale-level CVI (S-CVI) were calculated. I-CVI = Number of experts rating item as ‘relevance’ / (total number of experts) and S-CVI/Ave = (Σ of I-CVI scores) / (total number of items) [31]. An I-CVI ≥ 0.80 and an S-CVI ≥ 0.9 were deemed to indicate good content validity [32]. To assess construct validity, EFA and CFA were conducted. Before factor analysis, Kaiser‒Meyer‒Olkin (KMO) and Bartlett’s sphericity tests were used to verify sampling adequacy. Whenever the KMO value is above 0.60 and Bartlett’s test of sphericity is significant, the data are suitable for factor analysis [33, 34]. Varimax rotation and principal component analysis (PCA) were used in the EFA. The number of extracted factors was based on examination of the scree plot and eigenvalues > 1, and factor loading was assumed to be greater than 0.5. With CFA, a robust maximum likelihood method was used to evaluate the model’s fitness. Ideally, the chi-squared values divided by the degrees of freedom (CMIN/DF) should be smaller than 5, and the goodness-of-fit index (GFI), comparative fit index (CFI), and incremental fit index (IFI) should be greater than 0.9 [35].
The reliability of the Chinese version of the SIQ-SF was determined by analyzing its internal consistency and test-retest reliability. Cronbach’s α coefficient was used to evaluate internal consistency, and a value of > 0.70 indicated satisfactory internal consistency [36]. The intraclass correlation coefficient (ICC) was calculated to determine the test-retest reliability at an interval of 2 weeks, and the minimal acceptable value was set at 0.70 [37].
Rasch analysis
The RRSM was used to estimate item and person parameters. Several assumptions must be met before using the RRSM. Unidimensionality is one of the foundational assumptions, meaning that each item measures a single concept or construct [38]. Principal component analysis (PCA) for residuals was used to investigate potential unidimensionality [38]. The criteria for unidimensionality were that the variance explained by the measurement dimension be at least 40% and that the unexplained variance of the eigenvalue for the first contrast (size) be < 3.0 [38]. Additionally, local independence is another major assumption of Rasch models, which holds that items in a test are not related to one another [39]. Local independence can be assumed if Yen’s Q3 statistic is equal to or less than 0.36 [40]. To evaluate the items’ fit to the RRSM, the outfit mean square (outfit MnSQ) and infit MnSQ were computed, which should be between 0.6 and 1.4 [41]. The point-measure correlation (PTMEA CORR. ), which represents the relationship between an individual item’s response and the overall test score of an item, was used to investigate item polarity [42]. It is ideal for these correlations to be positive [42]. To ensure that items discriminate against different levels of person performance and that individuals can detect calibration differences between items, separation indices were calculated for items and persons. Person separation reliability measures the degree to which a respondent has maintained the same position when given another set of items measuring the same construct, whereas item separation reliability measures how consistently the set of items is answered by different respondents who possess similar abilities [43]. It is generally considered adequate if the separation index exceeds 2 and the separation reliability coefficient exceeds 0.7 [44]. To visualize item features and individual measures, a Wright map was used to estimate the values measured by the sample respondents and the average location of all items on a common scale (logits) [45]. Additionally, differential item functioning (DIF) was conducted to test measurement invariance across genders, and DIF is typically reported as a DIF > 0.5 logits with a p value < 0.05 [46].
Ethical considerations
Initially, permission to translate the SIQ-SF into Chinese was obtained from the original author. Ethical approval for the present study was received from the Research Management and Development Department of a College of Macau (No. REC-2021.801). The Helsinki Declaration was followed throughout the study. The data were used for research purposes only. Prior to participating in the survey, participants had to read the informed consent form and click the “Agree” button. Participants have the right to withdraw from the study without any negative effects.
Results
Demographic characteristics
A total of 1158 participants were included in the analysis, and their demographic characteristics are presented in Table 1. The participants ranged in age from 18 to 50 years, and the average age was 23 ± 5.0 years. Female participants accounted for 81.5%, and 72% of the participants had an education level of undergraduate or above.
Table 1
Demographic characteristics of the participants (n = 1158)
Variables | EFA (N = 579) | CFA & RA (N = 579) |
|---|---|---|
n (%) | n (%) | |
Age (mean, standard deviation) | 23.1 (4.8) | 23.1 (4.3) |
Gender | ||
Female | 466 (80.5) | 478 (82.6) |
Male | 113 (19.5) | 101 (17.4) |
Education | ||
Junior college | 160 (27.6) | 165 (28.5) |
Undergraduate | 333 (57.5) | 329 (56.8) |
Graduate | 86 (14.9) | 85 (14.7) |
Location | ||
Jiangsu | 142 (24.5) | 144 (24.9) |
Hunan | 154 (26.6) | 155 (26.8) |
Sichuan | 148 (25.6) | 139 (24.0) |
Macao | 135 (23.3) | 141 (24.3) |
Validity
Content validity
The I-CVI of the Chinese version of the SIQ-SF ranged from 0.9 to 1.0, and the S-CVI was 0.98, as shown in Supplementary Table S1. All the I-CVIs and S-CVIs were greater than 0.90, suggesting that the content validity of the SIQ-SF was good.
Construct validity
The construct validity was evaluated by EFA and CFA. The KMO value was 0.760, and Bartlett’s sphericity was statistically significant (χ2 = 1337.904, P < 0.001), indicating that the data were suitable for factor analysis. In the EFA, three factors - emotion, value, and intrinsic quality - were identified, all of which align with the original scale. These factors accounting for 62.554% of the cumulative variance. A scree plot displaying the eigenvalues is shown in Fig. 1. The factor loadings are presented in Table 2. Consequently, the three-factor model was examined by CFA. The results of CFA are shown in Fig. 2. The initial model was modified according to the modification indices. In the revised model, the CMIN\DF was 5.639, the GFI was 0.953, the CFI was 0.902, and the IFI was 0.904, indicating that the model fit the data adequately. The construct validity of the Chinese version of the SIQ-SF was found to be acceptable, with the identified factors and their corresponding items consistent with the original scale.
Fig. 1
Scree plot of the Chinese version of the SIQ-SF
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Fig. 2
The confirmatory factor model of the Chinese version of the SIQ-SF (N = 579)
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Table 2
Factor analysis results for the Chinese version of the SIQ-SF (N = 579)
Item | Emotion | Value | Intrinsic quality | |
|---|---|---|---|---|
Q8 | I am confident that I have chosen the major that corresponds to my personal preferences. | 0.809 | ||
Q9 | I chose my major primarily because of the interesting subject matter involved. | 0.745 | ||
Q3 | When I am in a library or bookstore, I like to browse through magazines or books having to do with topics related to my major. | 0.714 | ||
Q7 | Even before I started studying, my current major was important to me. | 0.693 | ||
Q4 | It was of great personal importance to me to be able to study this particular subject. | 0.682 | ||
Q5 | Compared to other things that are of great importance to me (e.g., hobbies, social life), my studies are of markedly less significance to me. | 0.856 | ||
Q2 | I prefer to talk about my hobbies rather than about my major. | 0.722 | ||
Q6 | Working with particular subject matter is more important to me than leisure and amusement. | 0.607 | ||
Q1 | After a long weekend or vacation, I look forward to getting back to my studies. | 0.590 |
Reliability
The Cronbach’s α coefficient of the SIQ-SF was 0.70, indicating that the internal consistency of the Chinese version of the SIQ-SF was satisfactory. Thirty-one participants were invited to complete the scale after two weeks. The ICC of the Chinese version of the SIQ-SF was 0.784, and that of the items ranged from 0.70 to 0.819, as shown in Supplementary Table 1. All the ICCs were greater than 0.70, suggesting that the test-retest reliability of the Chinese version of the SIQ-SF was good.
Rasch analysis results
Unidimensionality and local independence
Based on the PCA of the standardized residuals, the dimension extracted by the Rasch model accounted for 33.4% of the variance by persons and items, and the eigenvalue of the first contrast (the largest secondary dimension) was 2.3, which was considered appropriate. Furthermore, to ensure local independence, all pairs of items were thoroughly examined, and no pairwise residual correlation exceeded 0.5. Detailed information regarding this analysis can be found in Supplementary Table S2, which suggests that local dependence was not a concern.
Item fit
Infit and outfit indices indicated the construct validity of the assessment test for differentiating students with varying levels of learning interest. Table 3 provides the item statistics for each of the 9 items, including item difficulty, standard error of measurement, fit statistics (comprising infit and outfit), and point-measure correlation.
The infit MnSQ values ranged between 0.76 and 1.51, and the outfit MnSQ values ranged between 0.72 and 1.76. With the exception of Q5, which is slightly above the reference value, the goodness of fit values for all items ranged from 0.6 to 1.4. The minimum point-measure correlation value observed was 0.30, while the maximum value reached 0.68. These results indicate that the item in this scale aligns with other items for assessing nursing students’ learning interest.
Table 3
Item difficulty, standard error, fit, and point-measure correlation
Item number | Item difficulty | Standard error | Infit MnSQ | Outfit MnSQ | PTMEA CORR. |
|---|---|---|---|---|---|
Q1 | -0.38 | 0.07 | 1.05 | 1.04 | 0.50 |
Q2 | -0.53 | 0.08 | 1.37 | 1.31 | 0.30 |
Q3 | 0.16 | 0.07 | 0.80 | 0.77 | 0.64 |
Q4 | -0.62 | 0.08 | 0.90 | 0.87 | 0.57 |
Q5 | 1.44 | 0.06 | 1.51 | 1.76 | 0.36 |
Q6 | 0.18 | 0.07 | 0.91 | 0.92 | 0.53 |
Q7 | -0.66 | 0.08 | 0.76 | 0.72 | 0.59 |
Q8 | -0.05 | 0.07 | 0.84 | 0.81 | 0.68 |
Q9 | 0.44 | 0.07 | 0.81 | 0.80 | 0.67 |
Item and person reliability
In the Rasch model, reliability is a measure that indicates the consistency of the position of the person and item on the logit scale. According to Rasch analysis, the SIQ-SF had a person reliability coefficient of 0.70 and a person separation index of 1.51, indicating that the scores of the participants could be reliably estimated and classified into at least 2 strata [(1.51*4 + 1)/3]. The item reliability coefficient is 0.99, and the item separation index is 8.30. These findings suggest that these items provided accurate measurements and were effectively differentiated into at least 11 strata [(8.3*4 + 1)/3] of learning interest. A visual representation of the distribution of item difficulty and person ability is shown in Fig. 2. The right side of the Wright Map displays the distribution of items ranked by their difficulty, with the easiest items (Q4 and Q7) positioned at the bottom and the most difficult items (Q5) positioned at the top. The item difficulty measures ranged from − 0.66 to 1.44 logit with a spread of 2.1 logit. On the left side of the Wright Map, individuals are positioned. Nursing students with lower learning interest were situated at the base of the scale, while nursing students with higher learning interest were located at the top of the map. The individual learning interest estimations ranged from − 4.22 to 4.97 logit with a spread of 9.19 logit. The mean item difficulty for the test is 0.00 logits, while the mean person ability is approximately 0.75 logits higher. The dispersion of individuals in the Wright Map exhibits a significantly wider range compared to the spread of the test items.
DIF across gender
The DIF analysis determines whether constructs are equivalent across groups. Mean scores of the SIQ-SF based on demographic data are presented in Supplementary Table S3. Figure 3 compares the item DIF to the overall baseline item difficulty for the gender-based classification of people. For all items, DIF contrast ranged from 0.00 to 0.33 logits, with all p values greater than 0.05, showing no substantive DIF based on gender.
Fig. 3
DIF across genders for the Chinese version of the SIQ-SF
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Discussion
In the present study, the SIQ-SF was translated into Chinese, and the psychometric properties were determined among Chinese nursing students using CTT and IRT. Based on these findings, the Chinese version of the SIQ-SF demonstrated acceptable reliability and validity among Chinese nursing students. Educators can leverage this scale to gain a deeper understanding of nursing students’ learning interests. This understanding can inform the development of tailored content and pedagogical approaches, ultimately enhancing educational outcomes.
During the translation and cross-cultural adaptation, the modified Brislin model was rigorously followed to maintain semantic consistency [27]. The translation and cross-cultural adjustment did not alter the original structures of the SIQ-SF, and only a few items were modified to make them more easily understandable in Chinese. The S-CVI of the Chinese version of the SIQ-SF was greater than 0.90, demonstrating that the content validity of the Chinese version of the SIQ-SF was satisfactory. The Chinese version of the SIQ-SF maintained semantic consistency with the original scale, which may be partly due to the strict following of the guidelines of the theoretical model in the process of translation and cross-cultural adaptation [27]. On the other hand, it may also be that experts in various fields provided professional advice during the adjustment process.
According to the EFA, the Chinese version of the SIQ-SF had three factors (emotion, value, and intrinsic quality), which corresponded with the original scale. In the CFA, the model fit indices, including CMIN/DF, GFI, CFI, and IFI, all met the standard except for CMIN/DF. The CMIN/DF is a commonly used fit index in CFA, and it is a measure of the discrepancy between the observed covariance matrix and the model-implied covariance matrix, adjusted for degrees of freedom [47]. The CMIN/DF value was slightly greater than the normal range, indicating that there is still room for improvement. However, it is worth noting that the CMIN/DF ratio tends to increase as the sample size increases [48], and the large sample size in this study may partly account for the slightly greater CMIN/DF ratio. Additionally, all model fitting indices except CMIN/DF were within normal ranges, thus indicating an acceptable model fit.
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In terms of reliability, the Cronbach’s α coefficient and ICC were within normal ranges, indicating satisfactory internal consistency and test-retest reliability of the Chinese version of the SIQ-SF. The Cronbach’s α coefficient of this scale was 0.7, which was lower than that of the original version. Compared to the original scale, the Chinese version of the SIQ-SF has half as many items, which may explain the lower Cronbach’s α coefficient [49]. Regarding test-retest reliability, the ICC of the original SIQ was 0.67, which was lower than that of the Chinese version of the SIQ-SF [22]. In the present study, thirty-one participants were invited after two weeks, while the original SIQ was retested after two years. The various retest times may partly explain these differences [50, 51].
In addition to using CTT to verify the psychological properties of the scale, IRT was also used to obtain a comprehensive understanding of how survey items and respondents distributed and exhibited erratic behavior [52, 53]. Prior to applying the RRSM, the unidimensional nature of the scale and the local independence of the item were verified [54], and both assumptions were found to be true. In terms of item fit, the infit and the outfit MnSQ values of all items ranged between 0.6 and 1.4 except for Q5. The infit and outfit MNSQs of Item 5 exceeded the acceptable limits (1.51 and 1.76, respectively), so it might measure something other than or in addition to the learning interest [55]. Several potential reasons can contribute to this misfit. First, poorly constructed items, such as complicated language and double-barreled questions, can trigger inconsistent or unexpected responses and subsequently lead to Rasch model misfit [56]. Second, guessing behavior can result if the item is too easy or too difficult for the target population [57]. As shown in Fig. 4, Q5 was the most difficult item and was positioned at the top of the Wright map. It is possible that respondents may randomly guess the answer, leading to inconsistent responses and poor fit with the Rasch model. Despite not meeting the infit and outfit MNSQ of Q5, its point-measure correlation value was 0.36, indicating that Q5 was aligned with other items in assessing students’ learning interests. Accordingly, these 9 items fit the Rasch model adequately.
Fig. 4
Wright map of the Chinese version of the SIQ-SF
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DIF provides insight into how items perform in different groups and safeguards the validity of a scale [58]. An item is considered DIF when a characteristic apart from its ability influences the response of the respondent. In the present study, DIF analysis was performed across gender [58]. There was no significant difference in learning interest scores between male and female students. The DIF values for all the items were less than 0.5 logits, with all p values greater than 0.05, indicating that the DIF did not differ by gender. This result may be related to China’s social culture. It has traditionally been the case that nurses are dominated by women, but as society has become more diverse and inclusive, males are less influenced by gender expectations when selecting the nursing specialties in which they are interested [4]. Another possible reason is self-actualization. Today’s nurses perform a wide range of roles, including clinical nurses, nurse anesthesiologists, nursing educators, and nurse researchers [59]. As a result of this diversity, the nursing field creates a wider range of employment opportunities and attracts both males and females with diverse interests and ambitions.
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Limitations
There are several limitations to this study. First, only some nursing students from three provinces in eastern, central, and western China and one special administrative region participated in this study. This sample may not represent all Chinese or international nursing students. Consequently, generalizing the results to broader populations should be done with caution, and a more representative sample could be considered for further studies. Second, the data collection method used was self-reports on the online platform. Although each participant was allowed to answer the questionnaire only once, the process was still unregulated. In the future, other more effective methods may be considered. Third, although CTT and IRT were used to translate and validate the Chinese version of the SIQ-SF, a criterion validity test was not performed because of the lack of a gold standard for evaluating nursing students’ interest in learning. Finally, the study did not establish a definitive cut-off point for the scale. The absence of a validated cut-off point limits the interpretability and practical application of the findings, as it may affect the categorization of study interest levels among participants. Future research should focus on determining an appropriate cut-off point for the scale.
Conclusions
The Chinese version of the SIQ-SF demonstrated acceptable reliability and validity among Chinese nursing students and could be used to assess nursing students’ interest in learning in China. With the aid of this scale, teachers can gain a better understanding of nursing students’ learning interests, thereby maximizing their learning effects through appropriate content and methods. Future research should focus on determining an appropriate cut-off point for the Chinese version of the SIQ-SF and applying it to a larger population of college students to verify its psychological characteristics, thereby broadening its scope of application.
Acknowledgements
Not applicable.
Declarations
Ethics approval and consent to participate
This research was approved by the Research Management and Development Department of Kiang Wu Nursing College of Macau (No. REC-2021.801) and conducted according to the Declaration of Helsinki. The Research Management and Development Department is in charge of ethical approvals of scientific research projects, as well as issuing the ethical approval documents. It was a completely voluntary, anonymous, and unrewarded study. Informed consent was obtained from all the participants. The participants read and agreed to the informed consent before starting to fill in the questionnaire. In order to assured voluntariness, participants could withdraw at any time without losing benefits. Anonymity was guaranteed by not collecting any personal identification information (such as names). Confidentiality was ensured by storing data on a computer protected by a password known only by the researchers.
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Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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