From a total sample of 426 participants invited to participate in this study, 407 female participants responded (a 95.5% positive response rate). Most female participants were in the age group of younger than 30 years (44.5%), married (54.8%), at university (44.7%), house owners with personal property (64.1%), employees (63.6%), and with a monthly income ranging from 10,000–15,000 Riyals (32.2%), as seen in table 1. The mean age of the female participants was 33 years (SD ± 7.8).
Out of the total participants (407), only 287 females were car drivers (70.5%), as can be seen in Table 2, which describes their personal driving experience and practice. Most of the participants had a car (82.9%), recently started to drive within the last 2 years (74.6%), had insurance (85.4%), drove daily (66.2%), and drove for approximately 2 hours per day (51.9%); however, approximately half of the participants owned a car (49.8%), and some of them had already committed a traffic violation (43.6%).
Drivers’ attitudes toward certain traffic regulations: Table 3 illustrates female drivers’ attitudes toward certain traffic regulations. Almost all of the female drivers adhered to using a seat belt while driving (89.5%), and more than half had not received a ticket for a traffic violation (56.4%). However, most of them used their mobile phones frequently while driving for making calls (62.7%), receiving calls (67.9%), answering calls while driving (80.5%), and having received a ticket for committing a mobile violation (80.8%).
Table 4 illustrates the overall comparison scores for each domain of the DDDI, namely NCED, AD, and RD. We divided the overall scale into inadequate and adequate using the media for each category: NCED = 58.0, AD = 91.4, and RD = 82.9. We found that the overall prevalence of inadequate versus adequate was as follows: NCE was 99.7% versus 0.3%; AD was 24.7% versus 75.3%, and RD was 60.3% versus 39.7%.
In table 5, the associations between female participants’ NCED and sociodemographic statistics (age, marital status, education level, house ownership, employment, and monthly income) can be seen. In general, all of the mentioned characteristics did not have statistically significant relationships with NCED (p > 0.5); therefore, this domain was excluded in the further regression analyses. Further testing was applied between the sociodemographic characteristics and AD. There was a strong association between inadequate AD and marital status and inadequate AD and house ownership (p = 0.018 and p = 0.001, respectively). The other variables showed no significant association with AD. Moreover, almost all of the sociodemographic characteristics, such as education level (p = 0.001), marital status (p = 0.034), house ownership (p = 0.013), employment (p = 0.016), and monthly income (p = 0.003) showed a significant association with the inadequate RD of the female participants except for the age category, which showed no significant association (p > 0.05).
For the regression analysis models, as shown in table 6, only 2 models were implemented using the AD and RD subscales because the one related to NCED showed no association with any sociodemographic characteristics; therefore, it was excluded. For the backward multivariate analysis, after controlling for the sociodemographic factors, a strong association was found for inadequate AD between single/divorced/widowed participants (AOR: 1.692, 95% CI: 1.014–2.823, p = 0.044) and married participants, those with rented houses (AOR: 2.528, 95% CI: 1.287–4.966, p = 0.007) and those with their own property, and those with low and middle incomes (AOR: 3.105 and 2.269, 95% CI: 1.095–8.805 and 1.102-4.671, p = 0.033 and 0.026). On the other hand, a strong association was found for inadequate RD between single/divorced/widowed participants (AOR: 2.261, 95% CI: 1.249–4.092, p = 0.007) and married participants, employees (AOR: 0.475, 95% CI: 0.239–0.945, p = 0.034) and students/retirees/freelancers, and those with low–middle income (AOR: 0.348, 95% CI: 0.148–0.818, p = 0.016).
A Spearman correlation coefficient test was used first among the DDDI and then to correlate the DDDI with the independent sociodemographic variables. Findings from table 7, showed a significant positive correlation between NCED and AD and NCED and RD (r = 0.29, p < 0.001; and r = 0.1, p = 0.04, respectively). In addition, AD was shown to have a significant positive correlation with RD (r = 0.19, p < 0.001). The sociodemographic independent variables of age, education level, marital status, employment, and monthly income were correlated with the 3 domains of the DDDI. A significant negative correlation was found between NCED and both education level and marital status (r = −0.24, p < 0.001; and r = −0.13, p = 0.007, respectively). Moreover, RD was found to have a significant positive correlation with education level, employment, and monthly income (r = 0.16, p = 0.001; r = 0.9, p = 0.047; and r = 0.16, p = 0.001, respectively).