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Using Group Model Building to Capture the Complex Dynamics of Scaling Up District-Level Surgery in Arusha Region, Tanzania.

dc.contributor.authorBroekhuizen, Henk
dc.contributor.authorLansu, Monic
dc.contributor.authorGajewski, Jakub
dc.contributor.authorPittalis, Chiara
dc.contributor.authorIfeanyichi, Martilord
dc.contributor.authorJuma, Adinan
dc.contributor.authorMarealle, Paul
dc.contributor.authorKataika, Edward
dc.contributor.authorChilonga, Kondo
dc.contributor.authorRouwette, Etiënne
dc.contributor.authorBrugha, Ruairi
dc.contributor.authorBijlmakers, Leon
dc.date.accessioned2024-07-23T10:34:23Z
dc.date.available2024-07-23T10:34:23Z
dc.date.issued2020-12-13
dc.identifier.pmid33590734
dc.identifier.doi10.34172/ijhpm.2020.249
dc.identifier.urihttp://hdl.handle.net/10147/642470
dc.descriptionBackground: Scaling up surgery at district hospitals (DHs) is the critical challenge if the Tanzanian national Surgical, Obstetric, and Anesthesia Plan (NSOAP) objectives are to be achieved. Our study aims to address this challenge by taking a dynamic view of surgical scale-up at the district level using a participatory research approach. Methods: A group model building (GMB) workshop was held with 18 professionals from three hospitals in the Arusha region. They built a graphical representation of the local system of surgical services delivery through a facilitated discussion that employed the nominal group technique. This resulted in a causal loop diagram (CLD) from which the participants identified the requirements for scaling-up surgery and the stakeholders who could satisfy these. After the GMB sessions, we identified clusters of related variables using inductive thematic analysis and the main feedback loops driving the model. Results: The CLD consists of 57 variables. These include the 48 variables that were obtained through the nominal group technique and those that participants added later. We identified 6 themes: patient benefits, financing of surgery, cost sharing, staff motivation, communication, and effects on referral hospital. There are 5 self-reinforcing feedback loops: training, learning, meeting demand, revenues, and willingness to work in a good hospital. There are four self-correcting feedback loops or 'resistors to change:' recurrent costs, income lost, staff stress, and brain drain. Conclusion: This study provides a systems view on the scaling up of surgery from a district level perspective. Its results enable a critical appraisal of the feasibility of implementing the NSOAP. Our results suggest that policy-makers should be wary of 'quick fixes' that have short term gains only. Long term policy that considers the complex dynamics of surgical systems and that allows for periodic evaluation and adaption is needed to scale up surgery in a sustainable manner.en_US
dc.language.isoenen_US
dc.rights© 2022 The Author(s); Published by Kerman University of Medical Sciences This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectdistrict hospitalen_US
dc.subjectParticipatory researchen_US
dc.subjectSurgical Mentoringen_US
dc.subjectsystems thinkingen_US
dc.subjectTanzaniaen_US
dc.titleUsing Group Model Building to Capture the Complex Dynamics of Scaling Up District-Level Surgery in Arusha Region, Tanzania.en_US
dc.typeArticleen_US
dc.identifier.eissn2322-5939
dc.identifier.journalInternational journal of health policy and managementen_US
dc.source.journaltitleInternational journal of health policy and management
dc.source.volume11
dc.source.issue7
dc.source.beginpage981
dc.source.endpage989
refterms.dateFOA2024-07-23T10:34:25Z
dc.source.countryIran


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© 2022 The Author(s); Published by Kerman University of Medical Sciences This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as © 2022 The Author(s); Published by Kerman University of Medical Sciences This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.