Background: Patient satisfaction is a key indicator for improving healthcare delivery, yet the influence of gender preferences on healthcare providers remains underexplored. Cultural norms and gender perceptions often shape the patient preferences, affecting access to care, patient–provider relationships, and overall satisfaction. Thus, this study investigates the patients’ gender preferences and their impact on satisfaction in Tanzania. Methods: The study utilized a cross-sectional design, collecting data from five health centres: Mikongeni, Konga, Mzumbe, Tangeni, and Mlali. A total of 240 randomly selected respondents participated in the study. Gender preferences were categorized as male, female, and both, and determinants were analyzed using a multivariate probit model (MPM), while satisfaction was analyzed using an ordered logit model (OLM). Results: Results reveal that female providers were preferred for empathy (58.30%), intimate care (50.00%), and receptionist roles (50.00%), while males were favored for surgery (50.00%), professionalism (0.86), and IT roles (41.70%). Professionalism (0.75) and communication (0.70) had the strongest positive effects on very high satisfaction. Male provider preference was strongly linked to higher satisfaction (0.84), while female preference showed a mild effect (0.23). Insurance (0.32) and care at Tangeni Health Centre (0.70) boosted satisfaction, while consultation fees (−0.26) reduced it. Conclusions: The study recommends that healthcare systems address gender stereotypes by equipping all providers with both technical and relational care skills, regardless of gender. It also highlights the need for culturally and religiously sensitive care practices that acknowledge how societal norms shape patient preferences and satisfaction. To enhance patient-centered care, policies should promote affordability, broaden insurance coverage, and integrate patient feedback on gender preferences into healthcare delivery models.

Logistic regression (LR) has long been the standard econometric tool for modeling child nutritional outcomes in public health research. However, conventional econometric LR (CE-LR) faces limitations in predictive accuracy, reliance on restrictive assumptions, and handling high-dimensional data. Machine learning-enhanced LR (ML-LR)—which relaxes the strict statistical assumptions of traditional models to better capture complex patterns—combined with Shapley Additive Explanations (SHAP), offers a promising alternative, improving both prediction and interpretability of risk factors. This study presents the first Pakistan-specific application and comparison of ML-LR (with SHAP analysis) against CE-LR, introducing a novel hybrid framework that combines predictive power with interpretability for policy-relevant insights using nationally representative data from Pakistan’s 2017–2018 Demographic and Health Survey (n = 4,098 children under five). Results indicate persistent malnutrition rates: stunting (38.13%), underweight (23.04%), and wasting (8.05%). The ML-LR model identified all 13 hypothesized risk factors as significant, while CE-LR detected only six. Crucially, ML-LR captured key predictors missed by CE-LR, such as maternal BMI, employment, and dietary diversity. The SHAP analysis further revealed nuanced relationships: child age, low maternal BMI, unemployment, and unimproved water increased malnutrition risk, while higher birth order, adequate dietary diversity (if children were given ≥ 5 food items), maternal education, and male gender had protective effects. Crucially, ML-LR + SHAP uncovered context-dependent relationships invisible to CE-LR. For example, dietary diversity operated bidirectionally—low diversity was a risk factor, while adequate diversity was protective—a distinction CE-LR failed to capture. These findings demonstrate ML-LR’s superior ability to model complex, heterogeneous determinants of child malnutrition. The study advocates for integrating ML techniques with explainable AI (e.g., SHAP) in econometric analyses to enhance policy-relevant insights in public health.

This conceptual paper examines the influence of psychological principles on consumer decision-making within digital shopping environments, integrating psychological theories with empirical observations to understand how cognitive, emotional, and social factors shape consumer behaviors online. By reviewing existing literature and applying a constructivist epistemology and relativist ontology, the study develops a conceptual framework highlighting the impact of digital interface design, information availability, and personalized marketing on consumer choices. The paper analyzes various examples and identifies vital psychological triggers and decision-making barriers, offering strategic insights for enhancing digital marketing practices. This research contributes to academic and practical understandings of digital consumer behavior, proposing that a more profound integration of psychological insights can lead to more effective and ethically grounded marketing strategies in the digital era.

The management of solid waste from its collection to disposal is a widespread issue all around the world. This is a stinging issue in the rural and urban areas of the developing and developed states of the world. The mismanagement in Pakistan in the context of solid waste is required to be recognized because it is not only affecting the health of the public but also affecting the health of the environment. Therefore, this study conducts qualitative research methodology and conducted interviews with mega cities’ solid waste management officials and waste pickers, for analyzing uses Grounded theory through the lens of thematic analysis to highlight how public value is being destroyed by the mismanagement of solid waste in Pakistan. This study inspects and critically analyzes the effects of corruption, mismanagement, lawlessness, lack of accountability, budgetary issues, health, environmental and social inequalities issues, and no proper methods for the disposal of solid waste as the major causes that are destroying public value. The study emphasizes the significance of community awareness and active public participation in enhancing solid waste management practices. It also emphasizes the need for robust institutional frameworks and stricter enforcement of regulations to mitigate the risks associated with improper waste management. Moreover, the need for combined efforts by policymakers as well as governments, communities, and all relevant stakeholders is highlighted in the research to address the issue of sole waste management in developing states. To achieve a sustainable model for solid waste management that is able to protect the environment and public health, it is important to promote recycling, increase public awareness as well as improve waste segregation.

Background: Child malnutrition remains a serious public health challenge in Pakistan. The national health insurance initiative, the Sehat Sahulat Program (SSP), aims to improve access to health care for low-income families. This study examines whether the effectiveness of SSP follows the Law of Diminishing Marginal Returns (LDMR), whereby the marginal health benefits of insurance are greatest for the poorest households and decline with increasing wealth. Methods: We analyzed data on 4,499 children under 5 years of age in the Pakistan Demographic and Health Survey 2017–18. To address endogeneity and wealth-based heterogeneity, we employed an IV-Probit model and an IV-Quantile Regression (IV-QR) across wealth quintiles, using community-level internet access and distance to the nearest health facility as instrumental variables. Logistic regression was applied as a baseline model. Results: Analysis reveals a strong gradient in the effectiveness of SSP program. Insurance coverage is associated with reduced stunting and marked declines in diarrhea rates among the poorest households. These benefits have diminished in the overall distribution of wealth and have become statistically insignificant for the richest quintile. Econometric tests have confirmed a clear pattern of declining marginal returns. Conclusion: The Sehat Sahulat project is a very effective tool for reducing malnutrition among poor children in Pakistan. The observed pattern of diminishing returns suggests that SSP provides the greatest health benefits to the poorest households. These findings support a pro-poor targeting strategy in Pakistan to maximize the program’s impact within resource constraints.