Virtual multidisciplinary pain treatment: Experiences and feedback from children with chronic pain and their caregivers
Bibliographic record
Abstract
Background The onset of the coronavirus disease 2019 (COVID-19) necessitated a rapid transition to virtual care for chronic pain treatment.Objective This study examined experiences of patients and caregivers who received virtual multidisciplinary pain treatment (MDT) for pediatric chronic pain between March 2020 and August 2021.Methods A mixed methods design was implemented using qualitative interviews and quantitative satisfaction surveys. Satisfaction surveys were administered to a convenience sample of patients (aged 8 to 18; N = 20) and their caregivers (N = 20) who received MDT through an outpatient hospital pediatric chronic pain program. Interviews were conducted with a subset of these patients and their caregivers (n = 6).Results Analysis of interviews revealed four themes: 1) benefits of virtual care; 2) challenges of virtual care; 3) recommendations and evaluation of virtual care; and 4) patient preferences. Analysis of the satisfaction survey data revealed that while patients and caregivers were satisfied with many aspects of virtual care, 65% (n = 13) of patients reported a preference for in-person appointments, with caregivers showing equal preference for in-person and virtual appointments, though this was a non-significant difference (p = .37). Overall, both patients and caregivers stated a stronger preference for in-person physiotherapy sessions but were willing to have psychology sessions provided virtually. Finally, the most reported preference was for a hybrid model of care incorporating at least some in-person contact with providers.Conclusion This study provides a rich exploration of virtual care for multidisciplinary pediatric chronic pain treatment. The current results may inform the future development of guidelines for virtual care delivery with pediatric chronic pain populations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".