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.
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 | 0.185 | 0.198 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.068 | 0.018 |
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".