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Record W2736268678 · doi:10.1080/17439760.2017.1350741

Becoming who they want to be: A cross-national examination of value-behavior concordance and mindfulness in adolescence

2017· article· en· W2736268678 on OpenAlexaboutno aff
Michael T. Warren, Laura Wray‐Lake, Amy K. Syvertsen

Bibliographic record

VenueThe Journal of Positive Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
FundersClaremont Graduate UniversityJohn Templeton Foundation
KeywordsMindfulnessConcordancePsychologyMeditationMindfulness meditationMeaning (existential)Value (mathematics)Developmental psychologyPurpose in lifeClinical psychologyPsychotherapistMedicine

Abstract

fetched live from OpenAlex

Aristotle, Shakespeare, and Gandhi suggested the good life involves living according to one’s values, yet little research has examined the role of value-behavior concordance (VBC) in adolescence. Mindfulness may develop during adolescence and foster VBC via heightened awareness of one’s values and behaviors. We employed data from 5877 adolescents (M age = 17.53, SD = 3.67; 53% female) from seven countries (Australia, Canada, India, Thailand, Ukraine, United Kingdom, and United States), and examined invariance of links involving age, mindfulness, VBC, and meaning. In all countries, older adolescents were more mindful than younger adolescents, and higher levels of mindfulness were positively linked to VBC. Further, in six countries mindfulness was linked to meaning, in part, through VBC, suggesting a generalizable process where mindful youth experience greater meaning because they live according to their values. As evidence suggests mindfulness can be cultivated through meditation, mindfulness may be a trainable skill that helps young people become who they want to be.

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)0.003
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.433
Teacher spread0.364 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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