Rapid Evidence and Gap Map of virtual care solutions across a stepped care continuum for youth with chronic pain and their families in response to the COVID-19 pandemic
Bibliographic record
Abstract
ABSTRACT: Poor access to pediatric chronic pain care is a longstanding concern. The COVID-19 pandemic has necessitated virtual care delivery at an unprecedented pace and scale. We conducted a scoping review to create an interactive Evidence and Gap Map of virtual care solutions across a stepped care continuum (ie, from self-directed to specialist care) for youth with chronic pain and their families. Review methodology was codesigned with 8 youth with chronic pain and 7 parents/caregivers. Data sources included peer-reviewed scientific literature, gray literature (app stores and web sites), and a call for innovations. Records were independently coded and assessed for quality. Overall, 185 records were included (105 scientific records, 56 apps, 16 web sites, and 8 innovations). Most virtual care solutions were applicable across pediatric chronic pain diagnoses, with the greatest proportion at lower levels of stepped care (ie, >100 self-guided apps and web sites). Virtual delivery of psychological strategies was common. Evidence gaps were noted at higher levels of stepped care (ie, requiring more resource and health professional involvement), integration with health records, communication with health professionals, web accessibility, and content addressing social/family support, medications, school, substance use, sleep, diet, and acute pain flares or crises. Evidence and Gap Maps are a novel visual knowledge synthesis tool, which enable rapid evidence-informed decision-making by patients and families, health professionals, and policymakers. This evidence and gap map identified high-quality virtual care solutions for immediate scale and spread and areas with no evidence in need of prioritization. Virtual care should address priorities identified by youth with chronic pain and their families.
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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 | 0.091 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.040 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".