Implementation of policy and management interventions to improve health and care workforce capacity to address the COVID-19 pandemic response: living systematic review

Oliveira, Ana Carolina Mascarenhas; Galante, Mariana; Maia, Leila; Craveiro, Isabel; Pereira, Alessandra da Silva; Fronteira, Inês; Chança, Raphael; Cometto, Giorgio; Boniol, Mathieu; Ferrinho, Paulo; Poz, Mario

doi:10.18332/popmed/165571

Cited by 5 publications

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“…The framework also carries the potential of rapid use in the case of subsequent health emergencies to satisfy health workforce intelligence needs. The findings were broadly similar in terms of domains and issues documented, as were the policy responses identified [18][19][20][21][22]. The news and media sources led to an earlier identification of issues and challenges before the formal peer-reviewed literature could document them.…”

Section: Strengths and Limitationsmentioning

confidence: 65%

“…All the collective information around the COVID-19 pandemic and human resources for health resulted in a resolution on the Global Health and Care Compact (World Health Assembly Resolution WHA75/13 [17]). In addition, living systematic reviews aiming at information gathering and analytics on the health workforce in the context of the COVID-19 pandemic to support policy dialogue and advocacy opportunities have also been undertaken [18][19][20][21][22]. Complementary to the living systematic reviews, the WHO has been collecting strategic intelligence on the health workforce related to the COVID-19 pandemic from open sources in the form of Epidemic Intelligence through Open Sources (EIOS) system [23].…”

Section: Introductionmentioning

confidence: 99%

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Understanding COVID-19 Impacts on the Health Workforce: AI-Assisted Open-Source Media Content Analysis

Pienkowska,

Ravaut,

Mammadova

et al. 2024

JMIR Form Res

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Background To investigate the impacts of the COVID-19 pandemic on the health workforce, we aimed to develop a framework that synergizes natural language processing (NLP) techniques and human-generated analysis to reduce, organize, classify, and analyze a vast volume of publicly available news articles to complement scientific literature and support strategic policy dialogue, advocacy, and decision-making. Objective This study aimed to explore the possibility of systematically scanning intelligence from media that are usually not captured or best gathered through structured academic channels and inform on the impacts of the COVID-19 pandemic on the health workforce, contributing factors to the pervasiveness of the impacts, and policy responses, as depicted in publicly available news articles. Our focus was to investigate the impacts of the COVID-19 pandemic and, concurrently, assess the feasibility of gathering health workforce insights from open sources rapidly. Methods We conducted an NLP-assisted media content analysis of open-source news coverage on the COVID-19 pandemic published between January 2020 and June 2022. A data set of 3,299,158 English news articles on the COVID-19 pandemic was extracted from the World Health Organization Epidemic Intelligence through Open Sources (EIOS) system. The data preparation phase included developing rules-based classification, fine-tuning an NLP summarization model, and further data processing. Following relevancy evaluation, a deductive-inductive approach was used for the analysis of the summarizations. This included data extraction, inductive coding, and theme grouping. Results After processing and classifying the initial data set comprising 3,299,158 news articles and reports, a data set of 5131 articles with 3,007,693 words was devised. The NLP summarization model allowed for a reduction in the length of each article resulting in 496,209 words that facilitated agile analysis performed by humans. Media content analysis yielded results in 3 sections: areas of COVID-19 impacts and their pervasiveness, contributing factors to COVID-19–related impacts, and responses to the impacts. The results suggest that insufficient remuneration and compensation packages have been key disruptors for the health workforce during the COVID-19 pandemic, leading to industrial actions and mental health burdens. Shortages of personal protective equipment and occupational risks have increased infection and death risks, particularly at the pandemic’s onset. Workload and staff shortages became a growing disruption as the pandemic progressed. Conclusions This study demonstrates the capacity of artificial intelligence–assisted media content analysis applied to open-source news articles and reports concerning the health workforce. Adequate remuneration packages and personal protective equipment supplies should be prioritized as preventive measures to reduce the initial impact of future pandemics on the health workforce. Interventions aimed at lessening the emotional toll and workload need to be formulated as a part of reactive measures, enhancing the efficiency and maintainability of health delivery during a pandemic.

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Section: Strengths and Limitationsmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Understanding COVID-19 Impacts on the Health Workforce: AI-Assisted Open-Source Media Content Analysis

Pienkowska,

Ravaut,

Mammadova

et al. 2024

JMIR Form Res

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“…Building bridges between evidence, policy and implementation is therefore important, 17 but not an easy task. This endeavour leads to the politics and power dynamics of health systems strongly fragmented by sectors, organisational settings and professional groups including their deeply entrenched gender-based and ethnic/racial inequalities.…”

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confidence: 99%

The workforce crisis in healthcare: Moving the debate to bridge evidence and policy

Kuhlmann,

Lotta,

Dussault

et al. 2024

Health Planning & Management

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This Special Issue aims to advance the healthcare workforce (HCWF) debate by directing its attention to the implementation of policy recommendations and identifying weaknesses. The selection of articles highlights a wide range of HCWF policies and interventions across various countries. The challenges faced often stem from policy failures and governance gaps at the macro‐, meso‐ and micro‐levels of health systems. Recommendations to mitigate the HCWF crisis include interconnected strategies, multi‐/transsectoral policies, solidarity‐based efforts, collaboration, skill‐mix reforms, equity measures, global approaches, and crucially, strong political will. In addition, specific policy solutions are explored, such as community‐centred action and employment of community health workers, mental health support initiatives, inclusion of refugees and displaced healthcare workers into the labour market, and preparing the HCWF for the impact of climate change. This Special Issue calls for transformative HCWF policies and multi‐level transsectoral governance as essential components needed to effectively address the crisis. This will only be possible, if HCWF policy moves higher up in the public policy arena leading, among other things, to the establishment of HCWF research as a distinct academic field.

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Policies and Management Interventions to Enhance Health and Care Workforce Capacity for Addressing the COVID-19 Pandemic: Protocol for a Living Systematic Review

Cavalcante de Oliveira,

Lopes Galante,

Senna Maia

et al. 2023

JMIR Res Protoc

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Background Countries and health systems have had to make challenging resource allocation and capacity-building decisions to promote proper patient care and ensure health and care workers’ safety and well-being, so that they can effectively address the present COVID-19 pandemic as well as upcoming public health problems and natural catastrophes. As innovations are already in place and updated evidence is published daily, more information is required to inform the development and implementation of policies and interventions to improve health and care workforce capacity to address the COVID-19 pandemic response. Objective The objective of this protocol review is to identify countries’ range of experiences with policies and management interventions that can improve health and care workers’ capacity to address the COVID-19 pandemic response and synthesize evidence on the effectiveness of the interventions. Methods We will conduct a living systematic review of quantitative, qualitative, and mixed methods studies and gray literature (technical and political documents) published in English, French, Hindi, Portuguese, Italian, and Spanish between January 1, 2000, and March 1, 2022. The databases to be searched are MEDLINE (PubMed), Embase, SCOPUS, and Latin American and Caribbean Health Sciences Literature. In addition, the World Health Organization’s COVID-19 Research Database and the websites of international organizations (International Labour Organization, Economic Co-operation and Development, and The Health System Response Monitor) will be searched for unpublished studies and gray literature. Data will be extracted from the selected documents using an electronic form adapted from the Joanna Briggs Institute quantitative and qualitative tools for data extraction. A convergent integrated approach to synthesis and integration will be used. The risk of bias will be assessed with Joanna Briggs Institute critical appraisal tools, and the certainty of the evidence in the presented outcomes will be assessed with the Grading of Recommendations, Assessment, Development and Evaluation. Results The database and gray literature search retrieved 3378 documents. Data are being analyzed by 2 independent reviewers. The study is expected to be published by the end of 2023 in a peer-reviewed journal. Conclusions This review will allow us to identify and describe the policies and strategies implemented by countries and their effectiveness, as well as identify gaps in the evidence. Trial Registration PROSPERO CRD42022327041; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=327041 International Registered Report Identifier (IRRID) RR1-10.2196/50306

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Implementation of policy and management interventions to improve health and care workforce capacity to address the COVID-19 pandemic response: living systematic review

Cited by 5 publications

References 0 publications

Understanding COVID-19 Impacts on the Health Workforce: AI-Assisted Open-Source Media Content Analysis

Understanding COVID-19 Impacts on the Health Workforce: AI-Assisted Open-Source Media Content Analysis

The workforce crisis in healthcare: Moving the debate to bridge evidence and policy

Policies and Management Interventions to Enhance Health and Care Workforce Capacity for Addressing the COVID-19 Pandemic: Protocol for a Living Systematic Review

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