Can the Mindful Awareness and Resilience Skills for Adolescents (MARS-A) Program Be Provided Online? Voices from the Youth
Bibliographic record
Abstract
Mindfulness-based interventions (MBIs) have been shown to improve health and well-being in adolescents with chronic illnesses. Because they are most often delivered in person in a group setting, there are several barriers that limit access to MBIs for youth with limited mobility or who cannot access in-person MBIs in their communities. The objective of this study was to determine if eHealth is a viable platform to increase accessibility to MBIs for teens with chronic illnesses. This study reports the qualitative results of a mixed method randomized trial describing the experience of the Mindful Awareness and Resilience Skills for Adolescents (MARS-A) program, an eight-week MBI, delivered either in person or via eHealth. Participants were adolescents between the ages of 13 and 18 with a chronic illness recruited at a tertiary pediatric hospital in Toronto, Canada. Individual semi-structured post-participation audio-video interviews were conducted by a research assistant. A multiple-pass inductive process was used to review interview transcripts and interpret emergent themes from the participants' lived experiences. Fifteen participants (8 online and 7 in person) completed post-participation interviews. Four distinct themes emerged from participants in both groups: Creation of a safe space, fostering peer support and connection, integration of mindfulness skills into daily life, and improved well-being through the application of mindfulness. Direct quotations representative of those four themes are reported. Results from this study suggest that eHealth delivery of an adapted MBI for adolescents with chronic illnesses may be an acceptable and feasible mode of delivery for MBIs in this population. EHealth should be considered in future studies of MBIs for adolescents with chronic illnesses as a promising avenue to increase access to MBIs for youth who might not be able to access in-person programs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".