Self-directed learning and practice of Italian osteopathic students during summer break: a cross-sectional survey

Validation of Internal structure of Self-Directed Learning Readiness Scale among Indian Medical Students using factor analysis and the Structural equation Modelling Approach

BACKGROUND

The Self-Directed Learning Readiness Scale (SDLRS) is a tool that helps in the assessment of the readiness of the students to pursue Self-Directed Learning (SDL). There are no documented studies on the validation of internal structure of the SDLRS among Indian medical students. Hence, the objective of this study is to validate the internal structure of SDLRS among Indian medical students using factor analysis and the Structural Equation Modelling (SEM) approach.

METHODS

We administered Fisher's 40-item SDLRS to 750 students after receiving the ethics clearance and the author's permission and taking written informed consent from all the study participants (response rate: 92%). The exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Cronbach's alpha were performed using SPSS version 25 and the Lavaan package of R version 3.1.2.

RESULTS

The values of the comparative fit index (CFI), standardised root-mean-square residual (SRMR), and root mean square error of approximation (RMSEA) were ≥ 0.9, ≤ 0.08, and ≤ 0.08, respectively, for a model fit to be acceptable. EFA showed that except for Q2 (loading score: 0.210), Q12 (loading score: 0.384), Q13 (loading score: 0.362), and Q25 (loading score: -0.219), all the items loaded well. After the exclusion of the aforementioned items, the factor loading scores for the items in the self-management, desire for learning, and self-control factors ranged from 0.405 to 0.753 (Cronbach α: 0.775), 0.396 to 0.616 (Cronbach α: 0.730), and 0.427 to 0.556 (Cronbach α: 0.799), respectively. The updated model was used for CFA, which displayed a good model fit.

CONCLUSIONS

The resultant model consisting of 36 items is shown to have internal structure validity for Indian version of SDLRS, which can be used to assess medical students.

Osteopathy students profile in Italy: A cross sectional census

INTRODUCTION

The Osteopathy Students Analysis (OSA) aims to profile osteopathy students in Italy as a target population in terms of sociodemographic characteristics, geographical distribution, health status, and previous and ongoing education specifications.

MATERIALS AND METHODS

The OSA used a cross-sectional design. A Web-based survey was distributed to the Italian Osteopathic Education Institutions (OEIs). The OSA survey was composed of items organised into four sections: 1. Sociodemographic characteristics (11 items); 2. Geographical distribution (5 items); 3. Health status (3 items); 4. Previous and ongoing education specifications (16 items). A descriptive sample population analysis was performed. Dichotomous and categorical variables were presented as frequencies and percentages, and continuous variables were displayed as means and standard deviations. Some variables were analysed using a pentenary distribution.

RESULTS

49 out of the 61 OEIs identified matched the inclusion criteria, and among these, 22 accepted to propose the enrolment of their students into the study. The survey was administered to 4,720 students from all the participant OEIs. A total of 3,762 students responded to the survey, accounting for an estimated response rate of 53.7%. The majority of respondents were men (54%), with an average age of 26.9 ± 6.5 years. Almost the totality of the sample was composed of the European ethnic group (99.1%). Respondents were predominantly born in Italy (97.2%). The majority of the sample reported being in good (49.5%) to excellent (38.6%) health. To date, osteopathy students are almost evenly distributed between the two types of curricula (T1 = 46.6%; T2 = 53.4%).

CONCLUSIONS

The OSA is the first study that aims to profile Italian osteopathy students as a target population in terms of sociodemographic characteristics, geographical distribution, health status, and previous and ongoing education specifications. Future studies should focus on investigating the correlation between the sociodemographic characteristics of students and their academic performance.