Home Research Research Library Multinational primary health care experiences from the initial wave of the COVID-19 pandemic: A qualitative analysis Multinational primary health care experiences from the initial wave of the COVID-19 pandemic: A qualitative analysis 2022 Author(s) Taylor, Melina K, Kinder, Karen, George, Joe, Bazemore, Andrew W, Mannie, Cristina, Phillips, Robert L, Strydom, Stefan, and Goodyear-Smith, Felicity Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Teams Volume SSM: Qualitative Research in Health Source SSM: Qualitative Research in Health Objective: To learn from primary health care experts’ experiences from the COVID-19 pandemic across countries. Methods: We applied qualitative thematic analysis to open-text responses from a multinational rapid response survey of primary health care experts assessing response to the initial wave of the COVID-19 pandemic. Results: Respondents’ comments focused on three main areas of primary health care response directly influenced by the pandemic: 1) impact on the primary care workforce, including task-shifting responsibilities outside clinician specialty and changes in scope of work, financial strains on practices, and the daily uncertainties and stress of a constantly evolving situation; 2) impact on patient care delivery, both essential care for COVID-19 cases and the non-essential care that was neglected or postponed; 3) and the shift to using new technologies. Conclusions: Primary health care experiences with the COVID-19 pandemic across the globe were similar in their levels of workforce stress, rapid technologic adaptation, and need to pivot delivery strategies, often at the expense of routine care. ABFM Research Read all 2021 Empowering Family Physicians to Drive Change in Practice: Plans for the ABFM National Journal Club Go to Empowering Family Physicians to Drive Change in Practice: Plans for the ABFM National Journal Club 2021 Distribution of Physician Specialties by Rurality Go to Distribution of Physician Specialties by Rurality 2020 Using Machine Learning to Predict Primary Care and Advance Workforce Research Go to Using Machine Learning to Predict Primary Care and Advance Workforce Research 2025 Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model Go to Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model
Author(s) Taylor, Melina K, Kinder, Karen, George, Joe, Bazemore, Andrew W, Mannie, Cristina, Phillips, Robert L, Strydom, Stefan, and Goodyear-Smith, Felicity Topic(s) Role of Primary Care, and Achieving Health System Goals Keyword(s) Teams Volume SSM: Qualitative Research in Health Source SSM: Qualitative Research in Health
ABFM Research Read all 2021 Empowering Family Physicians to Drive Change in Practice: Plans for the ABFM National Journal Club Go to Empowering Family Physicians to Drive Change in Practice: Plans for the ABFM National Journal Club 2021 Distribution of Physician Specialties by Rurality Go to Distribution of Physician Specialties by Rurality 2020 Using Machine Learning to Predict Primary Care and Advance Workforce Research Go to Using Machine Learning to Predict Primary Care and Advance Workforce Research 2025 Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model Go to Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model
2021 Empowering Family Physicians to Drive Change in Practice: Plans for the ABFM National Journal Club Go to Empowering Family Physicians to Drive Change in Practice: Plans for the ABFM National Journal Club
2021 Distribution of Physician Specialties by Rurality Go to Distribution of Physician Specialties by Rurality
2020 Using Machine Learning to Predict Primary Care and Advance Workforce Research Go to Using Machine Learning to Predict Primary Care and Advance Workforce Research
2025 Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model Go to Evaluating primary care expenditure in Australia: the Primary Care Spend (PC Spend) model