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Record W2750028406 · doi:10.1177/0743558417722768

Self-Compassion From the Adolescent Perspective: A Qualitative Study

2017· article· en· W2750028406 on OpenAlexaff
Kirsten Klingle, K. Jessica Van Vliet

Bibliographic record

VenueJournal of Adolescent Research · 2017
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsPsychologySelf-compassionCompassionPerspective (graphical)Context (archaeology)Interpretative phenomenological analysisQualitative researchDevelopmental psychologySocial psychologyPsychotherapistMindfulnessSociology

Abstract

fetched live from OpenAlex

Although recent research has pointed to the potential benefits of self-compassion for youth, relatively little is known about the perspectives and lived experience of adolescents as they extend compassion toward themselves. The purpose of this phenomenological study was to gain an in-depth understanding of self-compassion from the adolescent perspective. Six adolescents, who had experienced a variety of life difficulties, were interviewed about their experiences of self-compassion in the context of their daily lives. Data were analyzed for common themes using interpretative phenomenological analysis. The resulting themes included putting oneself at the center, maintaining a positive outlook, engaging in pleasurable activities, connecting positively with others, working on self-improvement, making oneself attractive to others, accepting oneself, and experiencing emotional balance. Findings from this study add to self-compassion research by bringing attention to the contextualized meaning and experiences of self-compassion from the adolescent perspective. Directions for future research and limitations of the study are discussed.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.003
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 head0.217
GPT teacher head0.551
Teacher spread0.333 · 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
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

Citations41
Published2017
Admission routes1
Has abstractyes

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