Telemental Health For Youth With Chronic Illnesses: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Children, adolescents, and young adults with chronic conditions experience difficulties coping with disease-related stressors, comorbid mental health problems, and decreased quality of life. The COVID-19 pandemic has led to a global mental health crisis, and telemental health has necessarily displaced in-person care. However, it remains unknown whether such remote interventions are feasible or efficacious. We aimed to fill this research-practice gap. OBJECTIVE: In this systematic review, we present a synthesis of studies examining the feasibility and efficacy of telemental health interventions for youth aged ≤25 years with chronic illnesses. METHODS: PubMed, Embase, Web of Science, PsycInfo, and Cochrane Database of Systematic Reviews were searched from 2008 to 2020. We included experimental, quasiexperimental, and observational studies of telemental health interventions designed for children, adolescents, and young adults aged ≤25 years with chronic illnesses, in which feasibility or efficacy outcomes were measured. Only English-language publications in peer-reviewed journals were included. We excluded studies of interventions for caregivers or health care providers, mental health problems not in the context of a chronic illness, disease and medication management, and prevention programs for healthy individuals. RESULTS: We screened 2154 unique study records and 109 relevant full-text articles. Twelve studies met the inclusion criteria, and they represented seven unique telemental health interventions. Five of the studies included feasibility outcomes and seven included efficacy outcomes. All but two studies were pilot studies with relatively small sample sizes. Most interventions were based on cognitive behavioral therapy and problem-solving therapy. The subset of studies examining intervention feasibility concluded that telemental health interventions were appropriate, acceptable, and satisfactory to patients and their parents. Technology did not create barriers in access to care. For the subset of efficacy studies, evidence in support of the efficacy of telemental health was mixed. Significant heterogeneity in treatment type, medical diagnoses, and outcomes precluded a meta-analysis. CONCLUSIONS: The state of the science for telemental health interventions designed for youth with chronic illnesses is in a nascent stage. Early evidence supports the feasibility of telehealth-based delivery of traditional in-person interventions. Few studies have assessed efficacy, and current findings are mixed. Future research should continue to evaluate whether telemental health may serve as a sustainable alternative to in-person care after the COVID pandemic.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".