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Record W2129151639 · doi:10.1521/pedi.2012.26.6.939

What Does Sadness Mean to BPD Patients?

2012· article· en· W2129151639 on OpenAlexaff
Rachel Briand-Malenfant, Serge Lecours, Emilie Deschenaux

Bibliographic record

VenueJournal of Personality Disorders · 2012
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsSadnessPsychologyBorderline personality disorderContext (archaeology)DysphoriaDevelopmental psychologyClinical psychologyPsychotherapistAngerPsychiatryAnxiety

Abstract

fetched live from OpenAlex

Dysphoria is a core feature of Borderline Personality Disorder. Although a few studies have examined the nature of dysphoria in those patients, no research has focused on their experience of sadness. Considering the adaptive value of this emotion, an understanding of how BPD patients experience sadness is relevant to treatment. We conducted a qualitative analysis of the narratives of seven individuals with a diagnosis of BPD that have participated in a semi-structured interview describing sadness experiences in a relational context. Fourteen episodes were analyzed jointly by two doctoral students following Interpretative Phenomenological Analysis principles. Five themes were found: (1) aggression, (2) relationship broken off by the other, (3) undifferentiated negative affect, (4) self being defective, and (5) overwhelming experience. Surprisingly, sadness in a relational context was not associated with a representation of loss. The results suggest instead that sadness in those episodes is a non-mentalized experience that should not be conceptualized as sadness proper.

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)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.330
Teacher spread0.309 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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