Policies and Management Interventions to Enhance Health and Care Workforce Capacity for Addressing the COVID-19 Pandemic: Protocol for a Living Systematic Review
Bibliographic record
Abstract
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Organization, Economic Co-operation and Development, and The Health System Response Monitor) will be searched for unpublished studies and gray literature.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.141 | 0.132 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.023 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".