The Calm after the Storm: A State-of-the-Art Review about Recommendations Put Forward during the COVID-19 Pandemic to Improve Chronic Pain Management
Bibliographic record
Abstract
The COVID-19 pandemic has brought its fair share of consequences. To control the transmission of the virus, several public health restrictions were put in place. While these restrictions had beneficial effects on transmission, they added to the pre-existing physical, psychosocial, and financial burdens associated with chronic pain, and made existing treatment gaps, challenges, and inequities worse. However, it also prompted researchers and clinicians to seek out possible solutions and expedite their implementation. This state-of-the-art review focuses on the concrete recommendations issued during the COVID-19 pandemic to improve the health and maintain the care of people living with chronic pain. The search strategy included a combination of chronic pain and pandemic-related terms. Four databases (Medline, PsycINFO, CINAHL, and PubMed) were searched, and records were assessed for eligibility. Original studies, reviews, editorials, and guidelines published in French or in English in peer-reviewed journals or by recognized pain organizations were considered for inclusion. A total of 119 articles were analyzed, and over 250 recommendations were extracted and classified into 12 subcategories: change in clinical practice, change in policy, continuity of care, research avenues to explore, group virtual care, health communications/education, individual virtual care, infection control, lifestyle, non-pharmacological treatments, pharmacological treatments, and social considerations. Recommendations highlight the importance of involving various healthcare professionals to prevent mental health burden and emergency overload and emphasize the recognition of chronic pain. The pandemic disrupted chronic pain management in an already-fragile ecosystem, presenting a unique opportunity for understanding ongoing challenges and identifying innovative solutions. Numerous recommendations were identified that are relevant well beyond the COVID-19 crisis.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".