“Working Together”: Perspectives of Healthcare Professionals in Providing Virtual Care to Youth with Chronic Pain during the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The onset of the coronavirus disease in 2019 necessitated a rapid transition to virtual care for chronic pain treatment. Methods: A mixed methods design was implemented using qualitative interviews and quantitative satisfaction surveys. Interviews were conducted in February 2021 with a sample of healthcare professionals (HCPs; n = 6) who had provided multidisciplinary treatment (MDT) through an outpatient hospital pediatric chronic pain program. Satisfaction surveys were distributed to all MDT professionals employed by the clinic in April 2021 (n = 13 of 20 eligible; 65% response rate). Participants represented medicine, rehabilitation, and mental health professionals. Results: Analysis of interviews generated five themes: (1) adaptation to virtual care, (2) benefits of virtual care, (3) limitations of virtual care, (4) shifting stance on virtual care over time, and (5) considerations for implementing virtual care. The satisfaction survey data revealed that respondents were able to effectively provide appropriate diagnoses, recommendations, and/or care plans for pediatric chronic pain via virtual care (n = 12, 92.3%). Detailed survey responses are presented by discipline. Conclusions: This study provides a rich exploration of HCPs’ experiences in providing MDT for pediatric chronic pain within a virtual care model. The current results may contribute to 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 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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".