Bibliographic record
Abstract
Objective: Mental health concerns are rising, particularly among post-secondary students, who may lack access to traditional therapeutic resources due to barriers like long wait times and high costs. To help address these challenges, we explored the potential of large language model-based chatbots for supporting mental health and wellbeing in student populations.Methods: We conducted two studies, lasting one week and four weeks, to examine the effectiveness of chatbot interventions over different durations. Both studies compared two chatbot interventions—one mindfulness-focused and one value-focused—against an active check-in-only control condition. The primary outcome measure was the improvement in wellbeing through the mindfulness-to-meaning (MM) pathway, a process in which enhanced decentering, the ability to see one’s experience from a wider perspective, leads to improved positive reappraisal, the ability to find constructive and empowered interpretations of experience.Results: All conditions showed evidence of stress reduction. However, compared to the active control group, both intervention styles at both durations resulted in improved wellbeing via the MM pathway. This effect was primarily driven by significant improvements in decentering. For the longer duration only, we also observed enhanced reappraisal. Conclusions: These results emphasize the potential of chatbot-based interventions to support the development of regulatory skills by leveraging the MM pathway to enhance mental health. Educational institutions and mental health providers might consider integrating such tools into scalable and accessible student support systems, addressing a broader and more diverse audience while promoting sustained wellbeing through skills development.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".