Impact of the COVID-19 pandemic on the pharmacological, physical, and psychological treatments of pain: findings from the Chronic Pain & COVID-19 Pan-Canadian Study
Bibliographic record
Abstract
INTRODUCTION: Multimodal treatment is recognized as the optimal paradigm for the management of chronic pain (CP). Careful balance between pharmacological and physical/psychological approaches is thus desirable but can be easily disrupted. OBJECTIVES: This study aimed at exploring the impact of the COVID-19 pandemic on pharmacological and physical/psychological treatments of CP. METHODS: A Pan-Canadian cross-sectional web-based study was conducted between April 16th and May 31st 2020 among adults living with CP when the country was in the ascending slope of the first COVID-19 pandemic wave. RESULTS: A total of 2864 participants shared their treatment experience (mean age: 49.7 years and women: 83.5%). Among medication users (n = 2533), 38.3% reported changes in their pharmacological pain treatment. The main reasons were as follows: (1) changes in pain symptoms, (2) lack of access to prescribers/cancellation of medical appointments, and (3) increased medication intake in compensation for stopping physical/psychological treatments because of the pandemic. Among participants who used physical/psychological pain management approaches before the pandemic (n = 2467), 68.3% had to modify their treatments or self-management strategies. Common reasons were lack of access to clinics/exercise facilities and the need to compensate for having to stop another type of physical/psychological treatment because of the pandemic-related public health safety measures. CONCLUSIONS: Our study underlines the negative impact of the COVID-19 pandemic on access to pain relief, which is considered a fundamental human right. Results will help to justify resource allocation and inform the development of interventions to be better prepared for waves to come and future health crises.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".