Mindfulness-Based Interventions in Clinical Samples of Adolescents with Chronic Illness: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Mindfulness-based interventions (MBIs) have emerged as a promising strategy for individuals with a chronic illness, given their versatility in targeting both physical and mental health outcomes. However, research to date has focused on adult or community-based populations. OBJECTIVES: To systematically review and critically appraise MBIs in clinical pediatric samples living with chronic physical illness. DATA SOURCES: Electronic searches were conducted by a Library Information Specialist familiar with the field by using EMBASE, PsycINFO, MEDLINE, CINAHL, Web of Science, and EBM Reviews databases. Study Eligibility, Participants, and Interventions: Published English peer-reviewed articles of MBIs in clinical samples of children and adolescents (3-18 years) with chronic physical illness. STUDY APPRAISAL AND SYNTHESIS METHODS: Two reviewers independently selected articles for review and extracted data. Results are narratively described, and the reporting quality of each study was assessed via the STROBE Checklist. RESULTS: Of a total 4710 articles, 8 articles met inclusion criteria. All studies were small (n < 20, except 1 study of n = 59), included only outpatient adolescent samples, and focused on feasibility and acceptability of MBI; only 1 study included a comparison group (n = 1). No studies included online components or remote attendance. All studies found that MBI was acceptable to adolescents, whereas feasibility and implementation outcomes were mixed. Many studies were underpowered to detect significant differences post-MBI, but MBI did demonstrate improvements in emotional distress in several studies. Conclusions and Implications of Key Findings: The literature on MBIs is preliminary in nature, focusing on adapting and developing MBI for adolescents. Although MBIs appear to be a promising approach to coping with symptoms related to chronic illness in adolescents, future research with adequate sample sizes and rigorous research designs is warranted.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".