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Record W2907410887

Finding yourself through art education students experiences of an arts-based mindfulness group program

2018· dissertation· en· W2907410887 on OpenAlexaboutno aff
Patricia Grynspan

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2018
Typedissertation
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulnessThe artsGroup (periodic table)PsychologyVisual arts educationMedical educationPedagogyVisual artsMathematics educationPsychotherapistMedicineArt
DOInot available

Abstract

fetched live from OpenAlex

Postsecondary students’ mental health is a pressing concern on campuses across the
\ncountry as young adults are experiencing alarmingly high rates of stress-related challenges such
\nas anxiety and depression. A recent report released through the Council of Ontario Universities
\nrecommends postsecondary institutions work together with mental health systems to break down
\nsilos, find collaborative solutions, and implement programs focused on building resilience. My
\nresearch study explored the suitability and effectiveness of an innovative mindfulness-based
\nintervention (MBIs) called the Holistic Arts-Based Program (HAP) to teach mindfulness skills to
\neducation students. Arts-based methods are enjoyable and engaging, and enable individuals to
\nexpress feelings/thoughts that might otherwise be difficult to elicit; this information is rich and
\ninteresting, even powerful. Results of qualitative thematic analysis of pre- and post-group group
\ninterviews led to the development of three main themes: (1) increased self-awareness and
\nmindfulness, (2) benefits of arts-based methods, and (3) benefits of group work. Participation in
\nHAP helped students mitigate the negative impacts of stress, and taught them mindfulness
\nconcepts and activities that they were inspired to introduce into their education practicum. My
\nresearch demonstrates how interventions such as the HAP could make a difference in student’s
\nmental 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
\noptions for postsecondary students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.027
GPT teacher head0.298
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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