A cross-sectional study of pain status and psychological distress among individuals living with chronic pain: the Chronic Pain & COVID-19 Pan-Canadian Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a disproportionate impact on vulnerable populations, including individuals with chronic pain. We examined associations between geographical variations in COVID-19 infection rates, stress and pain severity, and investigated factors associated with changes in pain status and psychological distress among individuals living with chronic pain during the pandemic. METHODS: This investigation is part of a larger initiative, the Chronic Pain & COVID-19 Pan-Canadian Study, which adopted a cross-sectional observational design. A total of 3159 individuals living with chronic pain completed a quantitative survey between 16 April and 31 May 2020. RESULTS: Two-thirds (68.1%) of participants were between 40 and 69 years old, and 83.5% were women. Two-thirds (68.9%) of individuals reported worsened pain since pandemic onset. Higher levels of perceived pandemic-related risks (adjusted odds ratio: 1.27; 95% confidence interval: 1.03-1.56) and stress (1.21; 1.05-1.41), changes in pharmacological (3.17; 2.49-4.05) and physical/psychological (2.04; 1.62-2.58) pain treatments and being employed at the beginning of the pandemic (1.42; 1.09-1.86) were associated with increased likelihood of reporting worsened pain. Job loss (34.9% of individuals were employed pre-pandemic) was associated with lower likelihood (0.67; 0.48-0.94) of reporting worsened pain. Almost half (43.2%) of individuals reported moderate/severe levels of psychological distress. Negative emotions toward the pandemic (2.14; 1.78-2.57) and overall stress (1.43; 1.36-1.50) were associated with moderate/severe psychological distress. CONCLUSIONS: Study results identified psychosocial factors to consider in addition to biomedical factors in monitoring patients' status and facilitating treatment access for chronic pain patients during a pandemic.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".