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Record W4245530610 · doi:10.32920/ryerson.14664885

A laboratory examination of emotion regulation skill strengthening in borderline personality disorder

2021· preprint· en· W4245530610 on OpenAlexaff
Rebecca Metcalfe

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsToronto Metropolitan UniversityMcGill University
Fundersnot available
KeywordsBorderline personality disorderDistractionPsychologySkin conductanceClinical psychologyNegativity effectPersonalityEmotional regulationTask (project management)Developmental psychologyAudiologyCognitive psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

The present study examined emotion regulation skill strengthening among individuals with borderline personality disorder (BPD) compared to healthy controls (HCs). Participants were instructed to repeatedly implement two emotion regulation strategies (i.e., distraction and mindful awareness) in response to BPD-relevant stimuli across multiple trials. Throughout the task, both self-reported negativity and positivity, and physiological indices of emotion (i.e., heart rate and skin conductance response) were collected. Results indicated that individuals with BPD and HCs displayed improvements in distraction compared to the control condition, but not in mindful awareness over time. When comparing the two emotion regulation strategies to each other, rate of skill strengthening varied by group. Specifically, HCs evidenced improvements in distraction. In contrast, individuals with BPD evidenced improvements in mindful awareness. These findings suggest that individuals with BPD do not show deficits in skill strengthening as compared to HCs.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.318
Teacher spread0.296 · 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 designBench or experimental
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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