Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Asian Journal of Public Health and Nursing
Parsa, Ali Davod
Unknown Affiliation
Medicine & Pharmacology Nursing Public Health

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Mastering the Art of Scoping Reviews: A Comprehensive Guide for Public Health and Allied Health Students Kabir, Russell; Parsa, Ali Davod; Syed, Haniya Zehra; Bai, Ancy Chandrababu Mercy; Hussain, Remsha; Khan, Muhammad Feroz; Parvin, Sauda; Vinnakota, Divya; Sathian, Brijesh; Sivasubramanian, Madhini; Banerjee, Indrajit; Chowdhury, Mohammad Rocky Khan; Mohammadnezhad, Masoud; Arafat, S.M Yasir; Aaqib, Muhammad; Marthoenis, M; Husain, Syed Shajee; Hayhoe, Richard
Asian Journal of Public Health and Nursing Vol. 1 No. 2 (2024)
Publisher : Queeva Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62377/j544ed47

Abstract

Background: Scoping reviews systematically map the breadth of evidence on a particular topic, providing a comprehensive overview of the available research. This paper aims to outline the key steps involved in conducting a scoping review and to provide practical guidance for public health and allied health students and researchers. Methods: Formulating a research question using the PCC (Population, Concept, Context) framework to develop a clear research question or objective. Setting inclusion and exclusion criteria to guide the selection of studies for inclusion in the review. Conducting a thorough search across relevant databases and sources, including both academic and grey literature. Using a PRISMA flow diagram to document the search and selection process. Extracting and charting relevant data from included studies. Analysing synthesizing data using descriptive analysis or basic qualitative content analysis. Summarizing and presenting findings in a clear and meaningful way. Results: The paper provides a detailed guide for conducting scoping reviews, emphasizing the differences between scoping reviews and systematic reviews. It highlights that scoping reviews address broader research questions and typically do not assess study quality. Practical guidance is provided on developing search strategies and creating data extraction forms. Conclusions: This paper serves as a comprehensive guide for public health and allied health students and researchers undertaking scoping reviews, covering key methodological considerations and best practices throughout the review process.
Health Needs Assessment Plan for Pregnant Women in Low-Income Sub-Saharan Africa Anoh, Chinedu Okorie; Parsa, Ali Davod; Kabir, Russell; Hayhoe, Richard
Asian Journal of Public Health and Nursing Vol. 2 No. 2 (2025)
Publisher : Queeva Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62377/j303se37

Abstract

Maternal mortality remains a devastating public health challenge across Sub-Saharan Africa. Nigeria, in particular, accounts for nearly 20% of global maternal deaths, with a maternal mortality ratio (MMR) estimated at over 800 deaths per 100,000 live births (WHO, 2019). In Katsina State, recent hospital-based reviews report an MMR of approximately 1,200 per 100,000 live births, with hypertensive disorders and lack of antenatal care as leading contributors (Adeoye et al., 2025). This crisis is not merely statistical—it reflects systemic neglect, social inequity, and preventable loss. A Health Needs Assessment (HNA) offers a structured, evidence-based approach to uncovering these gaps and guiding targeted interventions. In Katsina, retrospective analyses show that over 68% of maternal deaths occurred in women who were not booked for antenatal care, and nearly half died within 24 hours of hospital presentation. These findings underscore the urgency of community-level engagement and early intervention. To move from data to action, multi-sectoral collaboration is essential. Corporate Social Responsibility (CSR) initiatives can play a transformative role in bridging funding and service gaps. The Centre for Social Justice (CSJ) has documented Katsina’s MNCH budget allocations and highlighted the disconnect between policy standards and actual health outcomes (CSJ, 2016). By aligning CSR investments with HNA priorities—such as mobile outreach, midwife training, and health literacy campaigns—private sector actors can contribute meaningfully to maternal health equity. This model is not unprecedented. CSR-health partnerships have yielded measurable improvements in maternal outcomes in India and Kenya (Ameh et al., 2012). Nigeria’s private sector, particularly in extractive and telecom industries, has the capacity to replicate and scale such interventions. What’s needed is political will, ethical commitment, and strategic alignment with community needs. Maternal mortality is not an inevitable consequence of poverty—it is a failure of systems, priorities, and imagination. A well-executed HNA, backed by CSR engagement and policy accountability, can reshape maternal health trajectories in Katsina and across low-income Sub-Saharan Africa. Let this be the moment we reframe maternal health not as a distant development goal, but as a shared responsibility—grounded in evidence, driven by compassion, and sustained by collaboration.
Machine Learning Applications in Suicide Prediction and Prevention: A Narrative Review Kabir, Russel; Ferdous, Nahida; Valand, Nirav; Kadhim, Zaid; Obaleye, Peter; Parsa, Ali Davod
Asian Journal of Public Health and Nursing Vol. 2 No. 3 (2025)
Publisher : Queeva Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62377/c9rj2z43

Abstract

Background: Suicide is a complex and preventable public health issue where traditional statistical techniques have shown limited effectiveness in predicting future suicide deaths. Machine learning offers promising approaches to identify complex patterns and improve prediction accuracy. Methods: This narrative review examined the application of machine learning in suicide prediction by searching academic databases (PubMed, CINAHL Plus, IEEE Xplore) using MeSH terms 'Machine Learning' and 'Suicide.' English-language articles published within the last five years focusing on suicide, suicide deaths, and prevention were included. The final selection comprised 18 articles after removing duplicates. Results: Key risk factors identified included mental health conditions (particularly depression), socioeconomic factors (unemployment and financial difficulties), family-related issues, and demographic characteristics (age, gender). Various machine learning approaches demonstrated effectiveness in predicting suicide risk. K-Nearest Neighbors and ensemble models (combining Random Forest and XGBoost) showed particularly strong performance. Time series models like ARIMA variants excelled at temporal predictions, while ensemble methods demonstrated versatility with multiple data sources. Conclusion: Machine learning techniques offer substantial improvements over traditional approaches for suicide prediction, with model selection dependent on data availability, geographical scale, and temporal requirements. Ensemble methods perform best with multiple data sources, while time series models excel with temporal data.
Co-Authors Aaqib, Muhammad Anoh, Chinedu Okorie Arafat, S.M Yasir Bai, Ancy Chandrababu Mercy Banerjee, Indrajit Chowdhury, Mohammad Rocky Khan Ferdous, Nahida Hayhoe, Richard Husain, Syed Shajee Hussain, Remsha Kabir, Russel Kabir, Russell Kadhim, Zaid Khan, Muhammad Feroz M. Marthoenis Mohammadnezhad, Masoud Obaleye, Peter Parvin, Sauda Sathian, Brijesh Sivasubramanian, Madhini Syed, Haniya Zehra Valand, Nirav Vinnakota, Divya