Mapping the Gaps: A Scoping Review of Virtual Care Solutions for Caregivers of Children with Chronic Illnesses
Bibliographic record
Abstract
Background/Objectives: Caregivers of children with chronic illnesses, including chronic pain, experience high levels of distress, which impacts their own mental and physical health as well as child outcomes. Virtual care solutions offer opportunities to provide accessible support, yet most overlook caregivers’ needs. We conducted a scoping review to create an interactive Evidence and Gap Map (EGM) of virtual care solutions across a stepped care continuum (i.e., from self-directed to specialized care) for caregivers of youth with chronic illnesses. Methods: The review methodology was co-designed with four caregivers. Data sources were the peer-reviewed scientific literature and a call for innovations. Records were independently coded and assessed for quality. Results: Overall, 73 studies were included. Most virtual care solutions targeted caregivers of children with cancer, neurological disorders, and complex chronic illnesses. Over half were noted at lower levels of stepped care (i.e., self-guided apps and websites), with psychological strategies being predominant (84%). However, very few addressed caregivers’ physical health (15%) or provided family counseling (19%) or practical support (1%). Significant gaps were noted in interventions for managing caregiver chronic pain, despite its high prevalence and impact on child outcomes. Conclusions: Evidence and Gap Maps are innovative visual tools for knowledge synthesis, facilitating rapid, evidence-informed decision-making for patients, families, health professionals, and policymakers. This EGM highlighted high-quality virtual care solutions ready for immediate scaling and identified critical evidence gaps requiring prioritization. To address the complexities of pediatric chronic illnesses, including chronic pain, virtual care initiatives must prioritize family-centered, accessible, and equitable approaches. Engaging caregivers as partners is critical to ensure interventions align with their needs and priorities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".