Reflections on teaching mindfulness to teenagers: from research to clinic
Bibliographic record
Abstract
Increased stress reactivity during adolescence has been associated with vulnerability for psychiatric disorders in adulthood and mindfulness-based interventions (MBI) seem to be an option to stress. However, there is still debate on how to best teach MBI to teenagers. For the last 6 years, authors have been proposing the “Mindfulteen” (MT) to teenagers between 12 and 19 years in Geneva. The MT was first applied in different clinical trials and in a qualitative study and, as the results were encouraging, is now proposed in a clinical context at the University Hospital. Authors aim to share here some lessons learned from this experience: 1. Motivation and curiosity are key to engagement, and this is particularly important in school settings; 2. Even if adaptation is needed for different age groups, the program’s core remains easily the same; 3. Short formal practices with not much silence are needed, and metaphors can help; 4. Clarifying the intention of each practice can improve engagement, and the same explicit attitude can be brought into inquiry; 5. A trauma-sensitive approach is crucial, especially in clinical settings; 7. Proposing different versions of the same practice facilitates home practice; 8. Even if participants are not practicing between sessions, it doesn’t mean that they are not integrating mindfulness into their lives; 9. Creative and playful activities can provide rich mindful moments. In conclusion, there are open questions about teaching mindfulness to adolescents and authors believe that sharing and exchanging experiences is important to find some of the answers.
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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 | 0.040 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".