Showing 1 - 3 results of 3 for search 'Cox's Bazar District', query time: 0.11s Refine Results
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    Challenges of healthcare financing in the world’s largest refugee camp: a mixed-method study among healthcare stakeholders for Rohingya refugees in Bangladesh by Isabel Smith, Imdadul Haque Talukdar, Sayeeda Tarannum, M A Rifat, Priyanka Boga, Jubayer Mumin, Pablo André Veronés, Ziba Mahdi, Syeda Saima Alam, Arif Faysal Khan

    Published 2025-01-01
    “…Financial documents (available online) and reports from respective coordinating agencies were also reviewed.Setting Online key informant interviews (KIIs) were conducted from Stockholm with participants residing in Bangladesh between June 2022 and September 2022.Participants Eight KIIs were conducted with professional health programme managers, executives and personnel involved in policy coordination and implementation in refugee camps in Cox’s Bazar district of Bangladesh.Results We identified four themes and three subthemes outlining key challenges in healthcare funding, including decreasing funds (supported by a quantitative assessment) due to macroglobal issues, conflicting short-term and long-term priorities between implementing partners, insufficient efficacy due to challenges with collaborative priority-setting and implementing common processes, and a lack of consensus regarding equity between host and refugee communities.Conclusions The study identified unique challenges beyond the commonly discussed health financing issues in a resource-deficient setting: stakeholders’ conflicting priorities regarding funding and undecided equity issues among the host and refugee communities are worth scholarly and policy focus. …”
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  2. 2

    Assessing the performance of machine learning and analytical hierarchy process (AHP) models for rainwater harvesting potential zone identification in hilly region, Bangladesh by Md. Mahmudul Hasan, Md. Talha, Most. Mitu Akter, Md Tasim Ferdous, Pratik Mojumder, Sujit Kumar Roy, N.M. Refat Nasher

    Published 2025-06-01
    “…Specifically, four ML algorithms—random forest (RF), boosted regression trees (BRT), k-nearest neighbors (KNN), and naïve bayes (NB)—alongside the analytical hierarchy process (AHP) were employed to delineate potential RWH zones in the Chattogram hilly districts, including Chattogram, Rangamati, Bandarban, Khagrachari, and Cox’s Bazar. …”
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