The national and global impact of systemic and structural violence on the effective prevention, treatment and management of COVID-19 in the African/Black population: Protocol for a Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND As the SARS-CoV-2 virus continues to ravage the globe and cases exploded rapidly, countries have been presented with challenging policy choices to contain the spread of COVID-19. In Canada, and globally, the COVID-19 pandemic has added a new stratum to the debate concerning the root causes of global and racial health inequities and disparities. Individuals who exist as targets of systemic inequities are not only more susceptible to contracting COVID-19, but they are also more likely to bear the greatest extent of the subsequent economic pandemic. Therefore, data collection that specifically focuses on the impact of COVID-19 on the lives and health of African/Black communities nationally and globally is needed to develop intersectional, culturally-relative, anti-racist/anti-oppression, empowerment-centered interventions and social policies to increase more efficient ways to support heterogeneous African/Black communities during and after the COVID-19 pandemic. OBJECTIVE The primary objective of this review is to investigate the impact and management of COVID-19 on African/Black individuals and communities in Canada and globally and understand how anti-black racism and intersectional violence impact the health of African/Black communities during the COVID-19 pandemic. Moreover, the study aims to explore scholarship pertaining to the impact of the COVID-19 on Black communities in the global context in multiple languages. We seek to determine how Black communities are impacted, so far as structural violence and systematic racism, health outcomes, and the ways in which attempts have been made to mitigate or manage the consequences of the pandemic and other injurious agents. METHODS A systematic search of published literature of quantitative and qualitative studies published on COVID-19 in Canada and globally will be conducted in Ovid Medline, Ovid EMBASE, EBSCO Cumulative Index to Nursing and Allied Health Literature, the Cochrane Library, Ovid PsychInfo, Ovid CAB Abstracts, Scopus, Web of Science, and Global Index Medicus. To be included in the review, studies should include data on COVID-19 in relation to African/Black individuals, population and communities in Canada and globally. The studies must discuss racism, oppression, anti-oppression, or systemic/structural violence and be published in English, French, Spanish, and Portuguese. The findings will be reported according to PRISMA-ScR. RESULTS Screening of title and abstracts from articles included from the aforementioned electronic databases concluded in 2022. Full-text screening and extraction is forthcoming given the large amount of literature remaining that appear to meet inclusionary criteria. Findings from the scoping review are expected to be published for peer review in Autumn 2022. CONCLUSIONS This review will collect important data and evidence on African/Black communities related to COVID-19. Moreover, this review could help identified existing gaps in COVID-19 management in the African/Black communities and inform future research. Furthermore, it could also be used in decision-making for health policy and promotion and can influence the services provided by healthcare facilities and community organizations around the globe. CLINICALTRIAL Open Science Framework (OSF). Submitted on November 1st, 2021
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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 | 0.083 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.089 | 0.016 |
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".