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Record W4404024964 · doi:10.22215/cujs.v3i1.4807

Why Practice Yoga? An Investigation of the Relationship Between Yoga Participation Motives, Sleep, and Mental Health

2024· article· en· W4404024964 on OpenAlexaff
Hannah Newsome, Cecilia Jorgenson

Bibliographic record

VenueCarleton undergraduate journal of science. · 2024
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsCarleton University
Fundersnot available
KeywordsSleep (system call)Mental healthPsychologyPsychotherapistClinical psychologyPhysical therapyMedicineComputer science

Abstract

fetched live from OpenAlex

Yoga is a holistic health approach practiced around the world due to its benefits on one’s physical, mental, and spiritual health (Mooventhan and Niyenthiha, 2020). Many people come to the mat for a variety of reasons not limited to losing weight, increasing strength, making friends, or reducing stress (Cartwright et al., 2020). The reasons we come to the mat may influence our adherence to participating in yoga, our mental health, and our physical health. This study seeks to find whether the motivations for why someone participates in yoga are associated with quality of sleep, anxiety, and the frequency of yoga participation over 6 months.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.395
Teacher spread0.311 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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