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Record W1896286531 · doi:10.4236/psych.2015.610121

Investigating Mindfulness, Borderline Personality Traits, and Well-Being in a Nonclinical Population

2015· article· en· W1896286531 on OpenAlexaff
Mabel Yu, Mitchell Clark

Bibliographic record

VenuePsychology · 2015
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsMount Royal University
Fundersnot available
KeywordsMindfulnessPsychologyFacet (psychology)Big Five personality traitsPersonalityClinical psychologyCorrelationPopulationDevelopmental psychologySocial psychologyDemography

Abstract

fetched live from OpenAlex

A growing body of literature has revealed that mindfulness-based interventions consistently have positive outcomes, suggesting that increased mindfulness is related to decreases in psychological symptoms and increases in well-being. In a sample of 110 Mount Royal University undergraduate students, we explored the intercorrelations between mindfulness, borderline personality traits, and subjective well-being (SWB). We hypothesized a negative correlation between mindfulness and borderline personality traits, a positive correlation between mindfulness and SWB, and a negative correlation between borderline personality traits and SWB. To examine, a battery of questionnaire containing four measures was used: Mindful Attention Awareness Scale (MAAS), Five Facet of Mindfulness Questionnaire (FFMQ), Personal Well-being Index (PWI), and Borderline Personality Questionnaire—revised (BPQ). Pearson’s correlation and multiple regression analysis results were consistent with our hypotheses. As predicted, higher degrees of mindfulness are associated with less borderline personality traits and greater well-being, whereas the presence of borderline personality traits is linked to lower degrees of well-being. The findings of the present study have significant clinical implications.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.077
GPT teacher head0.409
Teacher spread0.332 · 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.

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

Citations11
Published2015
Admission routes1
Has abstractyes

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