The Impacts of the COVID-19 Pandemic on the Lives of Disabled People: Disparities in Australia, Canada, the United Kingdom, and the United States
Bibliographic record
Abstract
The 2019 pandemic was a global health crisis caused by the Covid-19 novel coronavirus. As of 3 October 2023, 676,609,955 cases and 6,881,955 deaths were reported internationally (John Hopkins University, 2023). Everyone has been affected to some extent by the Covid-19 pandemic regardless of their status and situation in life. This paper focuses on the extent and ways the Covid-19 pandemic affected disabled and able-bodied groups of people and those who are classified as having “other” identities. It presents results from an online survey which collected responses from 1325 participants living in 52 countries. This paper will focus more specifically on the 1059 participants residing in Australia, Canada, the United Kingdom and the United States of America. The paper finds that a larger proportion of disabled participants were negatively impacted by Covid-19 than their able-bodied peers.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| 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".