Evidence map›Paper›PMID 42204584›Full record

ArticleHealth research policy and systems2026

Structural analysis and causal loop modelling: an integrated systems approach to analysing perinatal service delivery in carceral settings in Ontario, Canada.

Xaand Bancroft, Amy McGee, Lisa Morgan, Audrey Ferron Parayre, Wendy E Peterson

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Article in Health research policy and systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xaand BancroftUniversity of Ottawa, Interdisciplinary School of Health Sciences, Ottawa, ON, Canada. xbanc048@uottawa.ca.
Amy McGeeOttawa Hospital Research Institute, Ottawa, ON, Canada.
Lisa MorganUniversity of Ottawa, Interdisciplinary School of Health Sciences, Ottawa, ON, Canada.
Audrey Ferron ParayreUniversity of Ottawa, Faculty of Law, Civil Law Section, Ottawa, ON, Canada.
Wendy E PetersonUniversity of Ottawa, School of Nursing, Ottawa, ON, Canada.

Funding

Institut du savoir Montfort-Recherche Bourse de Doctorat (doctoral scholarship)
6 · The paper itself

Abstract

backgroundIn Ontario, Canada, incarcerated individuals and their newborns have adverse health outcomes. Perinatal care delivery in these settings is complex and fragmented across health, social service and carceral systems. This study aimed to apply and demonstrate an integrated systems-based analytical approach to examine dynamic interactions within carceral perinatal care and to identify influential variables and leverage points for policy and service improvement.

methodsUsing qualitative interview data from individuals who experienced incarceration while pregnant and from healthcare and social service providers, we applied an integrated methodological framework combining structural analysis (MICMAC) and causal loop diagramming. This stepwise approach was used to identify system variables, map causal pathways and feedback loops, and assess influence-dependence relationships within Ontario's carceral perinatal care system.

resultsOur analysis identified two key variables as central drivers of system behaviour through reinforcing and balancing loops, respectively: carceral staff autonomy and availability of providers. High levels of carceral staff autonomy reinforce power imbalances by decreasing providers' autonomy, which in turn constrains perinatal care delivery. Availability of providers operates within a balancing loop, whereby increased availability strengthens incarcerated clients' autonomy and increases their knowledge of human rights. Advocacy was identified as a key leverage point for navigating power dynamics within the system.

conclusionsThis study demonstrates the utility of integrating structural analysis with causal loop modelling to examine institutional dynamics, constraints and leverage points in complex health service systems. By identifying advocacy and comprehensive rights training as potentially essential interventions, alongside policy modifications, these findings highlight actionable pathways to reduce power imbalances and improve perinatal health outcomes among incarcerated individuals and their newborns. The approach provides a transferable framework for understanding complex and fragmented service delivery contexts and for informing evidence-based health policy and implementation strategies.

Indexed as

Delivery of Health CarePerinatal CarePrisonersFemaleHealth Services AccessibilityHealth Services for PrisonersHuman RightsHumansInfant, NewbornOntarioPregnancyPrisonsQualitative ResearchSocial WorkSystems AnalysisCarceral settingsCausal loop diagramHealth systemsIncarcerationMaternal healthPerinatal careReproductive justiceService delivery systemsStructural analysis (MICMAC)

Identifiers

PMID42204584
PMCPMC13292505

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.