Nigeria has one of the highest maternal mortality burdens globally. Improving maternal outcomes requires a better understanding of how women experience care across pregnancy, childbirth and the postnatal period. This study explored women’s maternal healthcare experiences across the perinatal continuum in Nigeria, with a focus on how challenges emerge and interact over time.

Longitudinal qualitative study using patient journey mapping.

Public primary, secondary and tertiary healthcare facilities in Abuja, Nigeria.

12 pregnant women were purposively sampled. Each woman participated in two rounds of in-depth interviews: once in late pregnancy and again 2–6 weeks postpartum. All participants completed both interview rounds.

Data were collected through 24 semistructured in-depth interviews conducted longitudinally to capture changes in women’s experiences before and after childbirth. Interview guides were informed by existing maternal health frameworks. Transcripts were analysed using reflexive thematic analysis and organised across five stages of the maternal healthcare journey: Awareness, Consideration, Access, Treatment and Recovery.

This study introduces a five-stage framework: Awareness, Consideration, Access, Treatment and Recovery, to comprehensively explore maternal healthcare experiences. The findings reveal systemic inefficiencies at every stage of the pregnancy journey, from limited awareness of pregnancy test kits to unreliable booking systems and inadequate postpartum mental health support. This study highlights how early-stage barriers cascade into later phases, unlike traditional research that focuses only on clinical interactions. This study emphasises the importance of maternal care accessibility and recovery support, moving beyond a treatment-centric lens. 

This study presents a transformative framework for understanding maternal healthcare as a continuum of interconnected experiences. The research offers actionable insights to enhance maternal health outcomes through stage-specific strategies. The globally adaptable framework provides policymakers and healthcare practitioners with a roadmap to improve maternal healthcare systems in Nigeria and beyond. This holistic approach lays the foundation for reducing maternal mortality while ensuring equitable care for all.

Artificial intelligence (AI) in healthcare often requires large, confidential clinical datasets. However, a recent UK government survey revealed that 20–40% of the public remain sceptical of its use in health research due to concerns about data security, patient–practitioner communication and commercialisation of data. A greater understanding of public attitudes is therefore needed, particularly in the context of stroke research.

In this article, we describe the patient and public involvement work undertaken for the AI-Based-Stroke-Risk-fActor-Classification-and-Treatment (ABSTRACT) project, which aims to train AI models to predict future stroke risk from the electronic health records of 1 18 736 patients.

We aimed to evaluate the opinions of stroke/transient ischaemic attack (TIA) patients, caregivers and members of the public on the following themes: (1) the acceptability of using AI to predict stroke from electronic health records, (2) obtaining these data using an opt-out model of consent and (3) allowing access to this dataset from members both within and outside of the routine clinical care team.

A total of 83 participants were recruited via the National Health Service social media and by approaching hospital inpatients. Participants were first provided with background information on stroke, AI in medical research and ABSTRACT’s proposed data handling protocol. A mixed methods approach was then used to explore each of the above themes using online survey, semistructured focus groups and one-to-one interviews.

Nearly all participants felt that it was appropriate to use patient data to train AI models to predict stroke risk and that it was acceptable to obtain these data via an opt-out model of consent. Almost all participants also agreed that data could be shared within and outside of the routine clinical care team, provided it was General Data Protection Regulation compliant and used for medical research only.

The public and those with lived stroke/TIA experience appeared to support using deidentified medical datasets for AI-driven stroke risk prediction under an opt-out consent model. However, this is provided that the research conducted is transparent, for a clear medical purpose and adheres to strict data security measures.

Despite the widespread use of community-engaged research (CEnR) in public health, there is a lack of practical guidance for ensuring research transparency while fostering collaboration between researchers and patient communities.

In this article, we propose the Five Nested Dolls Community-Engaged Research Framework (Five Dolls CEnR) as a tool to assist researchers in enhancing the transparency of CEnR and fostering collaboration between researchers and patient communities throughout all phases of CEnR.

Each of the five dolls represents a meaningful aspect of CEnR, such as patient engagement in research, conceptual framework, research design, findings and researchers’ positionality. In alignment with feminist standpoint theory, Five Dolls CEnR is based on a nesting design principle to demonstrate the influence of researchers’ experiences, perspectives, values, beliefs and assumptions on a research process.

To ensure transparency of the research process and foster collaboration in CEnR, the authors have described self-reflexivity and self-disclosure, two multidisciplinary concepts, as strategies. This framework consists of a series of steps and questions to promote self-reflexivity and self-disclosure of researchers at each doll level.

As a multidisciplinary framework, Five Dolls CEnR can be used across disciplines and throughout the planning, implementation and dissemination phases of a study.

Urban green and blue space (UGBS) interventions, such as the development of an urban greenway, have the potential to provide public health benefits and multiple co-benefits in the realms of the environment, economy and society. This paper presents the protocol for a 5-year follow-up evaluation of the public health benefits and co-benefits of an urban greenway in Belfast, UK.

The natural experiment evaluation uses a range of systems-oriented and mixed-method approaches. First, using group model building methods, we codeveloped a causal loop diagram with stakeholders to inform the evaluation framework. We will use other systems methods including viable systems modelling and soft systems methodology to understand the context of the system (ie, the intervention) and the stakeholders involved in the development, implementation and maintenance phases. The effectiveness evaluation includes a repeat cross-sectional household survey with a random sample of 1200 local residents (adults aged ≥16 years old) who live within 1 mile of the greenway. The survey is complemented with administrative data from the National Health Service. For the household survey, outcomes include physical activity, mental well-being, quality of life, social capital, perceptions of environment and biodiversity. From the administrative data, outcomes include prescription medications for a range of non-communicable diseases such as cardiovascular disease, type II diabetes mellitus, chronic respiratory and mental health conditions. We also investigate changes in infectious disease rates, including COVID-19, and maternal and child health outcomes such as birth weight and gestational diabetes. A range of economic evaluation methods, including a cost-effectiveness analysis and social return on investment (SROI), will be employed. Findings from the household survey and administrative data analysis will be further explored in focus groups with a subsample of those who complete the household survey and the local community to explore possible mechanistic pathways and other impacts beyond those measured. Process evaluation methods include intercept surveys and direct observation of the number and type of greenway visitors using the Systems for Observing Play and Recreation in Communities tool. Finally, we will use methods such as weight of evidence, simulation and group model building, each embedding participatory engagement with stakeholders to help us interpret, triangulate and synthesise the findings.

To our knowledge, this is one of the first natural experiments with a 5-year follow-up evaluation of an UGBS intervention. The findings will help inform future policy and practice on UGBS interventions intended to bring a range of public health benefits and co-benefits. Ethics approval was obtained from the Medicine, Health and Life Sciences Research Ethics Committee prior to the commencement of the study. All participants in the household survey and focus group workshops will provide written informed consent before taking part in the study. Findings will be reported to (1) participants and stakeholders; (2) funding bodies supporting the research; (3) local, regional and national governments to inform policy; (4) presented at local, national and international conferences and (5) disseminated by peer-review publications.