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Home Research Research Library Primary care financing: a systematic assessment of research priorities in low- and middle-income countries Primary care financing: a systematic assessment of research priorities in low- and middle-income countries 2019 Author(s) Goodyear-Smith, Felicity, Bazemore, Andrew W, Coffman, Megan, Fortier, Richard D W, Howe, Amanda, Kidd, Michael R, Phillips, Robert L, Rouleau, Katherine, and van Weel, Chris Volume BMJ Global Health Source BMJ Global Health Introduction Financing of primary healthcare (PHC) is the key to the provision of equitable universal care. We aimed to identify and prioritise the perceived needs of PHC practitioners and researchers for new research in low- and middle-income countries (LMIC) about financing of PHC. Methods Three-round expert panel consultation using web-based surveys of LMIC PHC practitioners, academics and policy-makers sampled from global networks. Iterative literature review conducted in parallel. First round (PreDelphi survey) elicited possible research questions to address knowledge gaps about financing. Responses were independently coded, collapsed and synthesised to two lists of questions. Round 2 (Delphi Round 1) invited panellists to rate importance of each question. In Round 3 (Delphi Round 2), panellists ranked questions in order of importance. Results A diverse range of PHC practitioners, academics and policy-makers in LMIC representing all global regions identified 479 knowledge gaps as potentially critical to improving PHC financing. Round 2 provided 31 synthesised questions on financing for rating. The top 16 were ranked in Round 3e to produce four prioritised research questions. Conclusions This novel exercise created an expansive and prioritised list of critical knowledge gaps in PHC financing research questions. This offers valuable guidance to global supporters of primary care evaluation and implementation, including research funders and academics seeking research priorities. The source and context specificity of this research, informed by LMIC practitioners and academics on a global and local basis, should increase the likelihood of local relevance and eventual success in implementing the findings. ABFM Research Read all 2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare 2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence 2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
Author(s) Goodyear-Smith, Felicity, Bazemore, Andrew W, Coffman, Megan, Fortier, Richard D W, Howe, Amanda, Kidd, Michael R, Phillips, Robert L, Rouleau, Katherine, and van Weel, Chris Volume BMJ Global Health Source BMJ Global Health
ABFM Research Read all 2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare 2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence 2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare
2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence
2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality
2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone