People with Disabilities and Other Forms of Vulnerability to the COVID-19 Pandemic: Study Protocol for a Scoping Review and Thematic Analysis
Bibliographic record
Abstract
OBJECTIVES: To develop a protocol for a scoping review mapping as well as thematically analyzing the literature on the effect of, and responses to, the coronavirus disease 2019 (COVID-19) pandemic, focused on people with disabilities with other layers of individual vulnerability or social disadvantage. METHODS: We will search scientific databases (Medline/PubMed, Web of Science, Scopus, AgeLine, PsycINFO, CINAHL, ERIC) and preprint servers (MedRxiv, SocArXiv, PsyArXiv). Google searches, snowballing, and key-informant strategies were also used, including a focus on the gray literature (eg, official reports). Peer-reviewed and preprint publications will be covered in 6 languages, and the gray literature in English. Publications will be included if they address individuals with disabilities; the COVID-19 pandemic or subsequent socioeconomic or occupational effects; and individual or social vulnerabilities, including any form of discrimination, marginalization, or social disadvantage. Two independent reviewers will perform eligibility decisions and key data extractions. Beyond mapping the literature, the results will thematically analyze any disproportionate risks people with disabilities and other forms of vulnerability experience in terms of being infected by COVID-19, having severe health consequences, and facing negative socioeconomic effects. Actions taken or recommended to reduce identified inequalities will also be synthesized. Our entire research team, with diverse backgrounds, will be involved in the synthesis. CONCLUSIONS: This review, which we plan to expedite, aims to inform policy makers, health authorities, disability advocates, and other stakeholders regarding the needs and ways to promote equity and disability-inclusive responses to the COVID-19 pandemic and the resultant socioeconomic shockwaves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".