南非卫生系统中社区主导监督的变革性影响:综合分析

Ndumiso Tshuma, Daniel Ngbede Elakpa, Clinton Moyo, M. Soboyisi, Sehlule Moyo, Sihlobosenkosi Mpofu, Martha Chadyiwa, Mokgadi Malahlela, Caroline Tiba, David Mnkandla, Tshepo M. Ndhlovu, Tsenolo Moruthoane, David D. Mphuthi, Oliver Mtapuri
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引用次数: 0

摘要

目标:社区主导监测(CLM)是一种新兴方法,它赋予当地社区积极参与卫生系统内数据收集和决策过程的权力。研究旨在探索利益相关者对社区主导监测数据的看法,并建立社区主导监测数据价值链,其中包括数据收集及其影响:方法:从参与南非卫生计划的利益相关者处收集定性数据。数据分析涉及一个合作研讨会,该研讨会整合了亲和图、主题分析和 Giorgi 方法中概述的系统编码过程等要素。研讨会促进了在开发数据价值链过程中的共同识别、共同创造知识和合作分析:结果:研究结果表明,CLM 数据能够进行社区层面的分析,促进计划宣传和地方合作。它提高了计划的重新设计、运营效率和快速反应能力。通过 CLM 数据价值链,针对具体情况的解决方案应运而生,促进了可持续和高效的计划实施:结论:CLM 是改进南非医疗保健计划实施、质量和宣传的有力工具。它通过让当地社区参与数据驱动的决策,加强了问责制、信任度和透明度。CLM 可应对特定环境下的挑战,并根据当地需求调整干预措施。
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The Transformative Impact of Community-Led Monitoring in the South African Health System: A Comprehensive Analysis
Objectives: Community-led monitoring (CLM) is an emerging approach that empowers local communities to actively participate in data collection and decision-making processes within the health system. The research aimed to explore stakeholder perceptions of CLM data and establish a CLM Data Value Chain, covering data collection and its impact.Methods: Qualitative data were collected from stakeholders engaged in health programs in South Africa. Data analysis involved a collaborative workshop that integrated elements of affinity diagramming, thematic analysis, and the systematic coding process outlined in Giorgi’s method. The workshop fostered joint identification, co-creation of knowledge, and collaborative analysis in developing the data value chain.Results: The findings showed that CLM data enabled community-level analysis, fostering program advocacy and local collaboration. It enhanced program redesign, operational efficiency, and rapid response capabilities. Context-specific solutions emerged through the CLM Data Value Chain, promoting sustainable and efficient program implementation.Conclusion: CLM is a powerful tool for improving program implementation, quality, and advocacy in South African healthcare. It strengthens accountability, trust, and transparency by involving local communities in data-driven decision-making. CLM addresses context-specific challenges and tailors interventions to local needs.
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