Although the BISP program greatly reduces poverty, people in the twin cities of Pakistan’s major cities do not yet know enough about the program through Instagram and Facebook. The research looks into how BISP uses social media on Facebook and Instagram to communicate with low-income households in Islamabad and Rawalpindi. A stratified sampling approach was employed with enhanced sample diversity considerations to improve generalizability. Quantitative surveys were used, and each of the 60 respondents was from either city to explore their use of social media, the amount of BISP content they saw, and how much they engaged with it. More than four out of five (83.3%) respondents are Facebook users, but more than half never find BISP content on social media; 70% prefer visual posts, and most (56.7%) still learn about the program by word of mouth. Because the strategies in use are not effective, BISP should use extra visual content, build a habit of posting regularly, clarify whether someone is eligible to use BISP, and get more involved with communities to close the information gap in major cities.

This study offers a comparative analysis of parametric, semi-parametric, and non-parametric techniques of survival data analysis. It focuses on assessing the methods in terms of their efficiency in estimating survival probabilities and hazard functions. Alternate real and simulated datasets are then utilized to assess the relative strengths and weaknesses of different approaches with respect to efficiency, flexibility, and interpretability. The results indicate that the choice of technique is a factor of the underlying data characteristics, with parametric models working quite well under specific assumptions, semi parametric methods balancing between the structure and flexibility, while the nonparametric ones have the best performance in scenarios driven by the data. The AIC and BIC values determined from the model selection process demonstrated that the model finding fits the best under the given conditions as it has the smallest AIC and BIC scores. This is because AIC and BIC balance between goodness of fit and complexity in the model. For the Cox proportional hazard model results, most variables tested in the analysis showed very few statistically significant p- values. Results from Kaplan-Meier survival analysis strengthened the model by providing critical survival probability key stages over time, indicating the most occurrences beyond 60 months marked significant declines in survival rates. Hence, since the determined model com- bines predictive accuracy with interpret-ability, it should have as few but robust elements as would be meaningful in survival prediction without over-fitting. Therefore, the final model selection is based on the best compromise between explanatory power and statistical validity.