Chronic pain experience and health inequities during the COVID-19 pandemic in Canada: qualitative findings from the chronic pain & COVID-19 pan-Canadian study
Bibliographic record
Abstract
BACKGROUND: Chronic pain affects about 20 % of the Canadian population and can lead to physical, psychological and social vulnerabilities. However, this condition remains poorly recognized and undertreated. During 2020, as the COVID-19 pandemic disrupted daily living and health care systems, the situation of people with chronic pain has drawn little public attention. METHODS: This qualitative study was part of a pan-Canadian mixed-methods project and aimed to understand the experiences and challenges of people living with chronic pain during the COVID-19 pandemic in Canada. Between May and August 2020, we conducted in-depth semi-structured interviews with 22 individuals living with chronic pain across the country. We used reflexive thematic analysis to interpret data. RESULTS: Our findings underscored four dimensions of the chronic pain experience during the pandemic: (1) Reinforced vulnerability due to uncertainties regarding pain and its management; (2) Social network as a determinant of pain and psychological condition; (3) Increasing systemic inequities intermingling with the chronic pain experience; (4) More viable living conditions due to confinement measures. Though several participants reported improvements in their quality of life and reduced social pressure in the context of stay-at-home orders, participants from socio-economically deprived groups and minorities reported more challenges in accessing pain relief, health care services, and psychosocial support. CONCLUSIONS: The COVID-19 pandemic has revealed and intensified pre-existing disparities and challenges among people living with chronic pain in terms of material resources, psychosocial condition, social support, and access to care. In post-pandemic times, it will be essential to address flaws in health and welfare policies to foster equity and social inclusiveness of people with chronic pain.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.005 | 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".