Exploring the Use of Large Language Models for Improving the Awareness of Mindfulness
Bibliographic record
Abstract
Teachable self-help techniques, such as mindfulness, can reduce anxiety and improve mental well-being outcomes. However, people lack proper awareness of such techniques. In this work, we explore the design space of using online dissemination channels to help people learn about mindfulness. We investigate the potential benefits of using Large Language Models (LLMs) to improve awareness and willingness to practice these techniques, building on a video-based intervention to introduce mindfulness. We designed a pilot between subjects randomized factorial experiment of 2 (Informational Chatbot: present vs. absent) x 2 (Tutorial Video: present vs. absent) x 2 (Reflection Chatbot: present vs. absent). Our preliminary findings suggest that interaction with either of the chatbots improved the participants’ intent to practice Mindfulness again, and the tutorial video improved the participants’ reported overall experience of the exercise. This highlights the potential promise and outlines the directions for exploring the use of LLM-based chatbots for awareness-related interventions.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".