Best practices for virtual care to support youth with chronic pain and their families: a rapid systematic review to inform health care and policy during COVID-19 and beyond
Bibliographic record
Abstract
The COVID-19 pandemic has acutely challenged health systems and catalyzed the need for widescale virtual care and digital solutions across all areas of health, including pediatric chronic pain. The objective of this rapid systematic review was to identify recommendations, guidelines, and/or best practices for using virtual care to support youth with chronic pain and their families (CRD42020184498). MEDLINE, CINAHL, Embase, APA PsychINFO, and Web of Science were searched the week of May 25, 2020, for English language peer-reviewed articles published since 2010 that (1) discussed children and adolescents aged <18 years reporting any type of chronic pain (ie, pain lasting >3 months); (2) focused on any type of virtual care (eg, telephone, telehealth, telemedicine, mHealth, eHealth, online, or digital); and (3) reported on guidelines, best practices, considerations, or recommendations for virtual care. Abstract and full text screening and data extraction were performed in duplicate. Meta-ethnography was used to synthesize concepts across articles. Of 4161 unique records screened, 16 were included addressing diverse virtual care and pediatric chronic pain conditions. Four key themes were identified: (1) opportunities to better leverage virtual care, (2) direct effective implementation of virtual care, (3) selection of virtual care platforms, and (4) gaps in need of further consideration when using virtual care to support youth with chronic pain and their families. No existing guidelines for virtual care for pediatric chronic pain were identified; however, best practices for virtual care were identified and should be used by health professionals, decision makers, and policymakers in implementing virtual care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.078 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".