Finding yourself through art education students experiences of an arts-based mindfulness group program
Bibliographic record
Abstract
Postsecondary students’ mental health is a pressing concern on campuses across the country as young adults are experiencing alarmingly high rates of stress-related challenges such as anxiety and depression. A recent report released through the Council of Ontario Universities recommends postsecondary institutions work together with mental health systems to break down silos, find collaborative solutions, and implement programs focused on building resilience. My research study explored the suitability and effectiveness of an innovative mindfulness-based intervention (MBIs) called the Holistic Arts-Based Program (HAP) to teach mindfulness skills to education students. Arts-based methods are enjoyable and engaging, and enable individuals to express feelings/thoughts that might otherwise be difficult to elicit; this information is rich and interesting, even powerful. Results of qualitative thematic analysis of pre- and post-group group interviews led to the development of three main themes: (1) increased self-awareness and mindfulness, (2) benefits of arts-based methods, and (3) benefits of group work. Participation in HAP helped students mitigate the negative impacts of stress, and taught them mindfulness concepts and activities that they were inspired to introduce into their education practicum. My research demonstrates how interventions such as the HAP could make a difference in student’s mental health. As MBIs may not have universal appeal they should not be a mandatory program requirement, but consideration may be given to offer mindfulness interventions as self-care options for postsecondary students.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".