1. The Scale
The SF-36 is a generic instrument for measuring health-related quality of life. It is a short, diversified health survey comprising just 36 items, divided into eight dimensions: general health (GH), physical functioning (PF), role limitations due to physical problems (RP), bodily pain (BP), social functioning (SF), role limitations due to emotional problems (RE), mental health (MH), vitality (VT), and one single item scale on health transition [10]. The SF-36 is suitable for self-administration, computerized administration, or administration by trained interviewer face to face or through phone call [11].
2. Local validation of the scale
Regarding the validation of our scale, the SF-36 was translated into Arabic and validated in Tunisia [12]. In order to guarantee the pertinence and reliability of the scale for use in the Tunisian context, the researchers followed a rigorous translation and cultural adaptation process. In this research, factor analysis in principal component with Varimax rotation allowed to extract two components: physical component (CM) and mental component (CP) which accounted for 62.3% of total variance [12]. The physical component (CP) is correlated with 4 scales: general health (GH), physical functioning (PF), role limitations due to physical problems (RP) and bodily pain (BP). The mental component (CM) is correlated with social functioning (SF), role limitations due to emotional problems (RE), mental health (MH) and vitality (VT).Moreover, their distribution is comparable to the original English and Italian versions of the SF-36 scale [10; 13; 14; 12]. This local validation strengthens the credibility of our research by certifying that our instrument precisely measures the dimensions of physical health and mental health in our population.
3. Study sample
Our study focuses on young employees aged between 18 and 35 within the Gafsa Mining Basin. According to the most recent census conducted by the NIS in Tunisia in 2014, the employed population (aged 15 and above) in our study area approximated 22,259 individuals2. Because of the absence of the total number of young employees aged between 18 and 35 in this population, we followed recommendations outlined by Hair et al. (2009), which suggest that the number of respondents should be at least eight times the number of variables under investigation [15]. In addition, to determine the n in order to estimate proportions, we worked with a confidence level of 95% and a margin of error of 5%. Therefore, 382 participants are sufficient [16] according to Filion et al (1990). Consequently, adhering to these guidelines, 400 responses proved more than adequate. With regard to the sampling method, we chose the quota sampling method because of the difficulty of obtaining such a list of young employees in our research area. This non-probabilistic approach aims to select participants effectively representing the target population. Quota sampling serves as a viable alternative when probabilistic methods are impractical due to the absence of a suitable sampling frame. Utilizing data from the latest census in Tunisia in 2014, which offers insights into the characteristics of the population under study, we employed a methodological approach. Although this data does not specifically outline the age group of 18 to 35 years, it formed the foundation of our sampling strategy. In addition, it is essential to bear in mind that the field of study is made up of 4 towns: Metlaoui, Moulares, Rdayef and Mdhila. Consequently, we focused on two key characteristics: "place of residence" and "field of work." The former allows us to accommodate variations among the four delegations of the Mining Basin, particularly regarding their demographic composition. Based on the latest population census, employees in our research area are distributed as follows: Metlaoui 37.09%, Moulares 23.20%, Rdayef 22.59% and Mdhila 17.10%. The latter facilitates the differentiation across various fields of employment. The participants, aged between 18 and 35 years, comprised 296 males and 104 females, engaging in voluntary participation. Our sample demonstrated diversity across five distinct work domains, primarily represented by the service sector (61.5%), followed by contributions from mining and energy (28.75%), industry (5.25%), construction and public works (3.25%), and agriculture (1.25%). This diversity mirrors the occupational spread among our participants, with 73.50% employed in the public sector and 26.50% in the private sector.
4. Ethical considerations
This study was conducted in conformity with the ethical principles of the Declaration of Helsinki and approved by the Ethics Committee from the University of Manouba. We took care to respect the dignity and rights of the participants, obtaining their informed consent prior to their participation. All participants were informed of the nature and aims of the study, and of their right to withdraw at any time without consequence. Care has been taken to minimise potential risks and to ensure the confidentiality of participants’ personal data. The results of this research will be shared transparently with the scientific community and the public, in accordance with the principles of the Helsinki Declaration on the Diffusion of Scientific Knowledge. In addition, the SF-36 questionnaire was used in this study, providing all the relevant references.
5. Statistical Methods Used for Data Analysis
After collecting the information, we used SPSS 21.0 to analyse the data. We started by checking the distribution of the data. We assessed the normality of the scores of the SF-36 variables using the Shapiro-Wilk and Kolmogorov-Smirnov tests. These analyses showed that the score distributions did not follow a normal distribution for the two components (p < 0.001) and for the two groups in our sample (p < 0.001). Consequently, the use of parametric tests was not appropriate. To meet our objective, we opted for non-parametric tests better suited to our data. The Mann-Whitney U test was chosen because it does not rely on the assumption of normality and allows us to compare the ranks of the data between two independent groups.