Stress and Pain Before, During and After the First Wave of the COVID-19 Pandemic: An Exploratory Longitudinal Mixed Methods Study
Bibliographic record
Abstract
Aims: This study explores the association between subjective feeling of stress and pain experience in the context of the COVID-19 pandemic with a focus on characteristics known to trigger a physiological stress response [sense of low control, threat to ego, unpredictability and novelty (STUN)]. Methods: This exploratory longitudinal convergent mixed methods design consisted of online questionnaires over three time points (before, during and after the 1st wave of the COVID-19 pandemic) (N = 49) and qualitative interviews (N = 27) during the 1st wave of the pandemic on distinct samples of individuals living with chronic pain (CP). Both types of data sources were mixed upon integration using joint display. Results: Mean pain intensity scores remained stable across time points, while pain unpleasantness and pain interference scores significantly improved. Global impression of change scores measured during the first wave of the pandemic do not entirely concord with pain scores evolution. Two thirds of participants reported a global deterioration of their pain condition at the beginning of the pandemic. Stress and pain catastrophizing before the pandemic were associated with pain scores throughout the pandemic; while most specific measures of stress due to the novel, uncontrollable, unpredictable and threatening nature of the pandemic were not. Qualitative data demonstrated that the deterioration reported in pain status reflected additional dimensions, including spatial expansion of the painful area, reduced access to treatments and challenges in adapting pain management strategies. Conclusions: Helping individuals to negotiate stressful aspects of the pandemic might help offset the negative impacts of stress on pain status in this context or other important life events.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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, 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".