ABSTRACT
Background: This qualitative study aimed to investigate the barriers that hinder men's utilisation of healthcare services in the Sedibeng district of South Africa. Methods: The study was conducted using flyers with questions posted on the Best Health Solutions' Facebook page for two weeks. A convenience sampling method was used and a total of 104 comments were collected from 64 respondents. The authors analysed the participants' self-reported data thematically on demographics, geographic area, and reasons for men not using healthcare services. Results: The findings revealed that sociocultural norms played a significant role in men's reluctance to seek medical help, as it was perceived as a sign of weakness. The scarcity of male healthcare professionals hinders open discussions. Stigma and discrimination were identified as substantial barriers. Convenience, trust, and confidentiality concerns, along with the influence of intimate partners, also influence men's decision-making. Conclusion: This study highlights the complex interplay between various barriers that impact men's utilisation of healthcare services in the district. By addressing these factors, healthcare providers and policymakers can enhance healthcare access and promote better health outcomes for men in Sedibeng district. Contribution: The manuscript's primary contribution lies in uncovering multifaceted barriers to men's healthcare utilisation in Sedibeng district. It explores socio-cultural norms, healthcare worker demographics, stigma, discrimination, convenience factors, trust, confidentiality concerns, and the influence of intimate partners on men's healthcare choices. These insights illuminate the complex factors affecting men's healthcare access, providing valuable knowledge for healthcare providers and policymakers.
ABSTRACT
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.