A Survey of the Challenges Faced by Individuals with Disabilities and Unpaid Caregivers during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic negatively affected many individuals. In particular, it is likely that individuals with disabilities and unpaid caregivers were disproportionately affected, however, its exact impact is largely unknown. The primary objective of this work was to identify challenges faced by individuals with disabilities and unpaid caregivers. A secondary objective was to identify potential solutions to the major challenges experienced by both populations. Two surveys were administered online to individuals with disabilities and unpaid caregivers, respectively between September 2020 and January 2021. We used an inductive thematic analysis within an interpretivist paradigm to analyze survey responses. A total of 111 survey responses were collected amongst both surveys. Separate thematic maps were created for individuals with disabilities and unpaid caregivers, and maps were drawn to compare challenges. Potential solutions to mitigate the challenges experienced by both populations include revising financial assistance programs and improving awareness of support programs that are available.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".