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An Integral Analysis of Mindfulness and Self-Compassion Among Adolescents

2018· book-chapter· en· W2907181994 on OpenAlexaff
Bernita Wienhold-Leahy

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2018
Typebook-chapter
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsMindfulnessPsychologyCompassionMindsetSelf-compassionEmpathyContext (archaeology)Grounded theoryPsychotherapistSocial psychologyQualitative researchPolitical scienceSociology

Abstract

fetched live from OpenAlex

This case study focused on teaching self-compassion to adolescents through a mindfulness program. Self-compassion involves being kind towards oneself, understanding that we are all part of common humanity, and mindfulness. This multi-methods study was grounded in integral theory, which examines self-compassion through multiple lenses with both qualitative and quantitative methodologies. The findings indicated that a mindfulness program teaching self-compassion had many benefits to students, including increased mindful awareness and focused attention; emotional awareness and regulation; self-awareness, self-kindness, and self-acceptance; resiliency and growth mindset; compassion, acceptance, and forgiveness for others; and a belief it could reduce bullying in schools. Mindfulness programs in the school context will need to be introduced slowly over the next several years as students, parents, teachers, and administrators all have to understand the importance of these skills before they can be implemented into the classroom.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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 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
Published2018
Admission routes1
Has abstractyes

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