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Record W6913006632 · doi:10.5683/sp3/pf9a3c

A Feasibility (pilot) Mixed Methods Study of an Innovative Non-Pharmacological Breath-Based Yoga and Social Emotional Intervention Program in an At-Risk Youth Sample in London, Canada.

2022· dataset· en· W6913006632 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsWestern University
Fundersnot available
KeywordsIntervention (counseling)AnxietyPopulationRandomized controlled trialSample (material)Perspective (graphical)Mental health

Abstract

fetched live from OpenAlex

Various service provision models for youth at risk of homelessness have been researched and implemented, including access to housing, physical and mental health resources, etc. However, there has been no alleviation in symptoms of depression and anxiety and the rate of drug use in these populations. This paper presents the results of a mixed-methods study in London, Canada, that examined the feasibility of implementing the SKY Schools intervention in at-risk youth aged between 16-25 (n=49). The study also recorded qualitative responses about the program’s usefulness from the perspective of the service users. The SKY schools intervention consisted of social-emotional learning combined with Sudarshan Kriya Yoga, a standardized yoga-based breathing exercise routine. The intervention program was divided into two phases; an active learning phase and a reinforcement phase. The results demonstrated that it is feasible to conduct a definitive trial in this population due to a high retention rate (61.2%) and overall positive feedback. Future researchers may consider the feedback received when designing a randomized control trial to further assess efficacy and tolerability.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.147
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.076
GPT teacher head0.416
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreDataset

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
Published2022
Admission routes2
Has abstractyes

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Same venueBorealisFrench-language works237,207