COVID-19 Pandemic Impact and Response in Canadian Pediatric Chronic Pain Care: A National Survey of Medical Directors and Pain Professionals
Bibliographic record
Abstract
The COVID-19 pandemic presents one of the greatest threats to pediatric pain care seen in generations. Due to public health restrictions, many pediatric pain clinics halted in-person appointments, delaying and disrupting access to care. There is no existing research on the impacts of COVID-19 on pediatric chronic pain care in Canada, nor the challenges experienced by healthcare professionals and pain clinics. To evaluate the impact of COVID-19 on Canadian pediatric chronic pain care by documenting how healthcare professionals provided care during the first six months of the pandemic. Two Canadian online cross-sectional surveys were conducted: one among Canadian pediatric pain clinic directors (Study 1) and another among multidisciplinary pediatric pain healthcare professionals (Study 2). : Responses from 13/13 Canadian pediatric pain clinics/rehabilitation programs indicated that all clinics provided virtual care during the pandemic. No significant changes were reported in the frequency of appointment requests. Most clinics reported no perceived change in patient pain levels (n=9/13, 69%) or occurrence of pain flares (n=10/13, 77%). Results from 151 individual healthcare professionals indicated that the majority (90%) of non-emergency department respondents were providing virtual care. The main challenges of virtual care included technological barriers, financial concerns, infrastructure and logistics, privacy, and clinical challenges. This study documented the impact of the COVID-19 pandemic on pediatric chronic pain care in Canada and highlighted the rapid shift to using virtual solutions. Simultaneously, respondents outlined current challenges and potential solutions to consider in the development of virtual care guidelines and policy in Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".