Currently, a variation of pedagogical approaches are used for improving student performance and increasing student achievement.^1,2 One of these approaches is Inter-Professional Education (IPE). IPE refers to situations where students from two or more health professions learn from each other to enhance collaborative practice, which in turn promotes health and treats patients.  In addition, IPE allows students to learn cooperation and teamwork in an integrated way to solve the problems of patients in a collaborative team environment.^3-5 A considerable amount of literature has been published on IPE. These studies show the importance of IPE in healthcare disciplines. For example, a systematic review shows that IPE experiences improve the knowledge and attitudes of learners towards interprofessional care, communication skills, problem- solving abilities and appropriate interactions.⁶  A further systematic review shows that IPE is associated with a range of positive outcomes in the health care system, such as decreased clinical errors, increased patient care, management, patient satisfaction, and teamwork. However, some studies based on this review showed that the IPE experiences did not affect patient care and practice.⁷ Buring and colleagues reported a piece of further evidence to support IPE. They indicated that IPE enhances teamwork, leadership, competencies and learning outcomes. However, they also highlighted barriers to IPE, such as logistical and resource issues.⁸

Given the importance that IPE has placed on the overall quality of patient care, medical educators have made concentrated efforts to construct scales to assess IPE in different cultures. For example, Lauffs and colleagues used the RIPLS to identify its validity and reliability for Swedish students.⁹ The RIPLS has been widely used in health-professions education as it is a valid and reliable scale for assessing the readiness of students for interprofessional learning.¹⁰ Because of this, many countries translated and adapted the RIPLS to determine the readiness of their students for IPE.^9-11 Along with this growth in IPE, however, there is an increasing concern over the lack of a valid and reliable Persian scale to measure the readiness of students for IPE. Therefore, we felt that it is essential to construct a Persian scale, which fits into a Persian culture. The purpose of this research is to determine whether or not the RIPLS can be adapted for Persian healthcare professions.

This study aimed to investigate whether the 4‐factor model from the original version of the RIPLS could be applied to the Persian translation of the scale. Because of this, the original version of the RIPLS was translated into the Persian language in order to identify its validity and reliability among medical students at Shiraz University of Medical Sciences.

The value of RMSEA suggests that the 4-factor model fits the population covariance matrix. It is a very useful fit index because of “its sensitivity to the number of estimated parameters in the model”.¹⁶  In our study, the CFI, which is usually estimated for checking goodness-of-fit a statistical model, is greater than the threshold (0.95) suggesting the 4-factor model fits an independent or null model. The GFI, which is analogous to R2 in multiple regression, indicates the proportion of variance in the sample explained by the estimated population covariance.¹⁷ The GFI is slightly less than the cut-off for a good fit. However, the AGFA indicates the 4-factor model fits the data. This suggests the 4-factor model needs to be cautiously applied for medical students. Finally, the NFI and NNFI were estimated to measure the disparity between the χ² value of the 4-factor model to the χ² value of the independent or null model. The values of the NFI and NNFI are indicative of a good –fitting model. Given that the values of goodness-of-fit statistics, it seems that the 4-factor model fits the sample data, although the two indices are a little below the threshold. Additional studies are necessary in order to adapt the 4-factor model for Iranian medical students. Furthermore, an inspection of the correlation between the loading estimates and the subscales in the path diagram, show the data fit the 4-factor model.

Cronbach’s alphas for the coefficients of the subscales TAC, NPI, PPI, and RAR in the Persian and other versions were approximately similar (0.89, 0.60, 0.86 and 0.28).^{9, 11, 18, 19} The low value of the RAR subscale could be due to the fact that the number of items is small. The key strength of this study is the high response rate from a convenience sample, and a number of important limitations need to be considered. First, this study was only conducted in a single institution; therefore, the generalisability of this study is limited.  Second, the study sample only consists of medical students and they may not be representative of other allied health students. Third, social desirability response bias may occur due to the nature of self-reports.  These study limitations suggest that further data collection is required to culturally adapt a Persian translated version of the readiness for interprofessional learning scale (RIPLS).

Taken together, the results of this study make several contributions to the current literature.  They suggest that the Persian translated version of the RIPLS may be a valid and reliable tool for assessing the attitudes of medical students in Iran. However, further research needs to need to be done to examine the subscales RAR and NPI before the scale is adapted for Persian healthcare students.