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Record W4238725924 · doi:10.32920/ryerson.14648217.v1

Delineating the unique contributions of mindfulness skills in predicting engagement in suicide attempts and non-suicidal self-injury among individuals with borderline personality disorder

2021· preprint· en· W4238725924 on OpenAlexaff
Lillian Krantz

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsToronto Metropolitan UniversityMcGill University
Fundersnot available
KeywordsBorderline personality disorderMindfulnessDialectical behavior therapyPsychologyClinical psychologyMediationPersonalityRandomized controlled trialPsychotherapistPsychiatryMedicineSocial psychologyInternal medicine

Abstract

fetched live from OpenAlex

The current research tested whether four dimensions of mindfulness – acceptance without judgment, observing, describing and acting with awareness – taught during 20 weeks of dialectical behavior therapy skills training (DBT-ST) predicted frequency of two forms of self-inflicted injury (SII), i.e. suicide attempts (SAs) and non-suicidal self-injury (NSSI), at baseline and mediated the relationship between pre-post treatment change in frequency of SAs/NSSI and DBT-ST. Eighty-four suicidal individuals with borderline personality disorder were enrolled in a single-blinded randomized trial comparing DBT-ST treatment to a waitlist control group. A series of regressions revealed no relationship between dimensions of mindfulness and self-inflicted injury at baseline. Although no significant effect of DBT-ST on SAs was found, a causal mediation analysis revealed acceptance without judgment significantly mediated the relation between DBT-ST and change in frequency of NSSI.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.016
GPT teacher head0.321
Teacher spread0.305 · 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
Published2021
Admission routes1
Has abstractyes

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