Transition to Virtual Care Services during COVID-19 at Canadian Pain Clinics: Survey and Future Recommendations
Bibliographic record
Abstract
Introduction: Due to the COVID-19 pandemic, healthcare centers quickly adapted services into virtual formats. Pain clinics in Canada play a vital role in helping people living with pain, and these clinics remained essential services for patients throughout the pandemic. This study aimed to (1) describe and compare the transition from in-person to virtual pain care services at Canadian pain clinics during the onset of the COVID-19 pandemic and (2) provide postpandemic recommendations for pain care services to optimize the quality of patient care. Materials and Methods: We used a qualitative participatory action study design that included a cross-sectional survey for data collection and descriptive analysis to summarize the findings. Survey responses were collected between January and March of 2021. The survey was administered to the leadership teams of 11 adult pain clinics affiliated with the Chronic Pain Centre of Excellence for Canadian Veterans. Responses were analyzed qualitatively to describe the transition to the virtual pain services at pain clinics. Results: We achieved a 100% response rate from participating clinics. The results focus on describing the transition to the virtual care, current treatment and services, the quality of care, program sustainability, barriers to maintaining virtual services, and future considerations. Conclusions: Participating clinics were capable of transitioning pain care services to the virtual formats and have in-person care when needed with proper safety precautions. The pandemic demonstrated that it is feasible and sustainable for pain clinics to have a hybrid of virtual and in-person care to treat those living with pain. It is recommended that moving forward, there should be a hybrid of both virtual and in-person care for pain clinics. Ministries of Health should continue to develop policies and funding mechanisms that support innovations aimed at holistic healthcare, interdisciplinary teams, and the expansion of clinics' geographical reach for patient access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".