Acceptability, Appropriateness and Feasibility of a Calibrated Obstetric Blood Collection Drape for Postpartum Haemorrhage Detection: A Qualitative Study in Two Tertiary-Care hospitals in Delhi, India
Manna, S.; Dhiman, P.; Lyngdoh, T.; Munshi, H.; Goel, S.; Suri, J.; Gupta, M.; Kumari, R.; Bajaj, B.; Joshi, R. K.; Dhume, P. V.; Mukherjee, R.
Show abstract
ABSTRACT Background: Postpartum haemorrhage (PPH) is the leading cause of maternal mortality globally. Accurate and objective blood loss measurement is essential for timely detection and management of PPH. However, visual estimation remains the predominant method of blood loss estimation in low- and middle-income country settings, despite it underestimating blood loss by 33-50%. In this context, a calibrated obstetric blood collection drape offers a practical, low-cost alternative. However, Evidence on acceptability, appropriateness and feasibility remains limited. Objective: To explore the facilitators and barriers to the acceptability, appropriateness and feasibility of the routine implementation of a calibrated obstetric blood collection drape for postpartum haemorrhage (PPH) detection in two tertiary-care hospitals in Delhi, India. Methods: This qualitative study used semi-structured interviews to explore healthcare providers' experiences and perceptions regarding the acceptability, appropriateness and feasibility of using the drape in routine labour room practice. Qualitative data were analysed using thematic analysis. Results: Eighteen healthcare providers participated in qualitative interviews. The calibrated drape was perceived as more reliable than visual estimation and easy to use, supporting confidence, quicker responses and clinical decision-making. It was also considered useful for early PPH detection and timely management. Key challenges included staff shortages and concerns about drape slipperiness and fastening mechanisms. Conclusions: Findings suggest that the calibrated obstetric blood collection drape is perceived as acceptable, appropriate and feasible for routine use in high-volume tertiary-care hospitals in India. Wider implementation will require reliable supply chains, design refinements, and sustained training programmes, including for support staff.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Adoption of policies to improve respectful maternity care in Timor-Leste 96%
- Implementing intravenous iron for maternal anemia in Nigeria: A qualitative study of healthcare provider experiences using the Normalization Process Theory 96%
- Identification and Mitigation of High-Risk Pregnancy with the Community Maternal Danger Score Mobile Application in Gboko, Nigeria 96%
Similar papers in this journal
- Implementation and Evaluation of Obstetric Early Warning Systems in tertiary care hospitals in Nigeria 97%
- Community-based newborn care intervention fidelity and its implementation drivers in South Wollo Zone, North-east Ethiopia 96%
- Factors influencing the motivation of maternal health workers in conflict setting of Mogadishu, Somalia 95%
Similar papers in this journal
- Cost saving in primary versus tertiary level of reproductive health services in Sana’a, Yemen 95%
- Experience of induction of labour: a cross-sectional postnatal survey of women at UK maternity units 95%
- Women’s Awareness of Obstetric Fistula and its Associated Factors Among Reproductive-Age Women in Ethiopia: A Multilevel Analysis Based on National Survey Data 95%
Similar papers in this journal
Similar papers in this journal
- Knowledge, attitude and practice toward COVID-19 among healthcare workers in public health facilities, Eastern Ethiopia 94%
- Knowledge, Attitude and Practice towards COVID-19 among people in Bangladesh during the pandemic: a cross-sectional study. 92%
- Hospital based contact tracing of COVID-19 patients and health care workers and risk stratification of exposed health care workers during the COVID-19 Pandemic in Eastern India 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.