From Pandemic to Progress: Maternal Health Resilience in the post COVID-19 era in Tamil Nadu, India DOI Creative Commons
Kandaswamy Paramasivan, Ashwin Prakash

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Abstract Background and Objectives The COVID-19 pandemic considerably impacted emergency medical services (EMS), particularly in the context of maternal care. In response, government made significant investments both EMS health during pandemic. This study aims to evaluate childbirth outcomes, specifically resilient period, by analyzing long-term effects on healthcare delivery. Data Methods research analyzed key metrics related for pregnancy, including call volume, response transfer times, hospital handoff ambulance travel distances. Maternal outcomes assessed included mortality rates, institutional childbirth, home deliveries, miscarriages, vaginal complications, C-section rates. was sourced from Tamil Nadu State Control Room registry, covering historical data Jan 2017 phases 2020–2022 subsequent period 2023-24. employs time-series analysis compare distribution daily eight with average frequency pre-pandemic period. An effect size measure is then used quantify improvement metrics. Results Throughout various stages pandemic, there a notable increase volume women. Despite this, were improvements times. comparison corresponding before saw marked enhancements post phase 2023 2024. Specifically, rate dropped 19%, 37 deaths per 100,000 live births, significantly lower than national 97 births. Additionally, rates infant mortality, neonatal complicated deliveries decreased 19.35%, 17.03%, 28.02%, 19.23%, 36.05%, respectively. Conclusions: Government along sustained focus programs, appear have provided substantial support pregnant women newborns. reproductive does not seem been adversely

Language: Английский

“Because Doulas Save Black Women’s Lives”: Black Women’s Strategic Use of Doulas in Anticipation and Experiences of Obstetric Racism DOI
Sharon Casapulla, Sarah Rubin,

Belainesh Nigeda

et al.

Women s Reproductive Health, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 21

Published: May 9, 2025

Language: Английский

Citations

0
Generative AI for Qualitative Analysis in a Maternal Health Study: Coding In-depth Interviews using Large Language Models (LLMs) DOI Creative Commons
Shan Qiao,

Xingyu Fang,

Camryn Garrett

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Study Objectives In-depth interviews are one of the most widely used approaches for qualitative studies in public health. The coding transcripts is a critical step information extraction and preliminary analysis. However, manual often labor-intensive time-consuming. emergence generative artificial intelligence (GenAI), supported by Large Language Models (LLMs), presents new opportunities to understand human languages, which may significantly facilitate process. This study aims build computational framework that uses GenAI automatically detect extract themes from in-depth interview transcripts. Methods We conducted an experiment using with maternity care providers South Carolina. leveraged ChatGPT perform two tasks automatically: (1) deductive coding, involves applying predefined set codes dialogues; (2) inductive can generate dialogues without any preconceptions or assumptions. fine-tuned content transcripts, enabling it summarize codes. then evaluated performance proposed approach comparing generated those manually coders, involving human-in-the-loop evaluation. Results results demonstrated potential detecting summarizing could be utilized both processes. overall accuracy higher than 80% showed high positive associations manually. More impressively, reduced time required 81%, demonstrating its efficiency compared traditional methods. Discussion models like show generalizability, scalability handling large datasets, proficient multi-level semantic structure identification. They demonstrate promising making valuable tool supporting people health research. challenges such as inaccuracy, systematic biases, privacy concerns must addressed when them practice. GenAI-based should handled caution reviewed coders ensure reliability.

Language: Английский

Citations

2
From Pandemic to Progress: Maternal Health Resilience in the post COVID-19 era in Tamil Nadu, India DOI Creative Commons
Kandaswamy Paramasivan, Ashwin Prakash

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Abstract Background and Objectives The COVID-19 pandemic considerably impacted emergency medical services (EMS), particularly in the context of maternal care. In response, government made significant investments both EMS health during pandemic. This study aims to evaluate childbirth outcomes, specifically resilient period, by analyzing long-term effects on healthcare delivery. Data Methods research analyzed key metrics related for pregnancy, including call volume, response transfer times, hospital handoff ambulance travel distances. Maternal outcomes assessed included mortality rates, institutional childbirth, home deliveries, miscarriages, vaginal complications, C-section rates. was sourced from Tamil Nadu State Control Room registry, covering historical data Jan 2017 phases 2020–2022 subsequent period 2023-24. employs time-series analysis compare distribution daily eight with average frequency pre-pandemic period. An effect size measure is then used quantify improvement metrics. Results Throughout various stages pandemic, there a notable increase volume women. Despite this, were improvements times. comparison corresponding before saw marked enhancements post phase 2023 2024. Specifically, rate dropped 19%, 37 deaths per 100,000 live births, significantly lower than national 97 births. Additionally, rates infant mortality, neonatal complicated deliveries decreased 19.35%, 17.03%, 28.02%, 19.23%, 36.05%, respectively. Conclusions: Government along sustained focus programs, appear have provided substantial support pregnant women newborns. reproductive does not seem been adversely

Language: Английский

Citations

0