Exploring the Experiences of People with Disabilities during the First Year of COVID-19 Restrictions in the Province of Quebec, Canada
Bibliographic record
Abstract
During the COVID-19 pandemic, the province of Quebec, Canada implemented stringent measures to mitigate virus transmission, which considerably affected the life of people with disabilities (PWD). The objective of this study was to explore the experiences of PWD during the first year of COVID-19 restrictions across the province. Participants who self-identified as having a disability in the Ma Vie et la pandémie study (MAVIPAN) were invited to participate in a semi-structured interview between December 2020 and May 2021. A mixed inductive and deductive approach was used to conduct a thematic analysis using NVivo 12. Forty PWD from Quebec, Canada participated in the interviews (mean [SD] age, 55.4 [15.5] years, 50% women). A deterioration in mental health and a reduction in social contact with loved ones were reported. PWD experienced delays and cessation of health services and reported feeling at risk of contracting severe strains of COVID-19 because of their health condition. Enhanced difficulties experienced by PWD and the lack of consideration specific to PWD by public authorities during COVID-19 was particularly concerning for participants in this study. Future studies should explore the value of implementing social programs specifically targeting PWD to enhance support as the pandemic continues.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".