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Record W4407872950 · doi:10.1016/j.amp.2025.02.010

Contribution des formes de tristesse aux symptômes du trouble de personnalité borderline

2025· article· fr· W4407872950 on OpenAlexafffund
Serge Lecours, Gabrielle Riopel

Bibliographic record

VenueAnnales Médico-psychologiques revue psychiatrique · 2025
Typearticle
Languagefr
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

L’expérience des individus souffrant d’un trouble de la personnalité borderline (TPB) est empreinte d’affects dysphoriques douloureux. Des recherches antérieures ont distingué cette dysphorie de la dépression majeure, soulignant sa nature relationnelle, chargée d’hostilité et indifférenciée. Nous souhaitons poursuivre cette exploration des affects dysphoriques douloureux dans le TPB en formulant la définition de la dysphorie en termes de différentes qualités de tristesse. L’échantillon de 208 étudiants de premier cycle (âge moyen : 22,4 ; 87,5 % de femmes) a complété deux questionnaires en ligne : le Questionnaire sur les formes de tristesse (QFT) et l’outil de dépistage McLean Screening Instrument for BPD (MSI-BPD). Le MSI-BPD est utilisé comme une mesure dimensionnelle de traits TPB auto-identifiés. Le QFT évalue deux qualités de la tristesse : la tristesse pathologique (tristesse autocritique) et la tristesse adaptative (tristesse tolérée). Des corrélations ont été effectuées pour examiner les relations entre les variables principales. Une analyse de régression hiérarchique a été menée afin d’évaluer la contribution unique des tristesses pathologique et adaptative dans l’estimation des traits du TPB, en tenant compte de la contribution du sexe et de l’âge. Le nombre de critères du TPB auto-rapportés est plus fortement et positivement associé à une tristesse autocritique et, dans une moindre mesure, négativement à une tristesse tolérée. Ainsi, les résultats indiquent que les deux formes de tristesse, ainsi que l’âge, expliquent 30,8 % de la variance des traits de TPB autodéclarés, le prédicteur le plus important étant la tristesse pathologique. La dysphorie du TPB pourrait donc être composée d’au moins deux types d’expériences subjectives « dépressives » ou dysphoriques : (1) principalement un type de tristesse pathologique ; et (2) un manque de tristesse adaptative. Painful dysphoric affects are an important part of the phenomenology of individuals suffering from borderline personality disorder (BPD). Previous research has distinguished this dysphoria from major depression, underlining its relational, hostility-laden, and undifferentiated nature. We have previously noted that the processing of loss was absent from BPD individuals’ discussion of sadness-laden interactions and that lower levels of mentalization of sadness were strongly associated with BPD traits. We wish to pursue our exploration of painful depressive affects in BPD by framing the definition of dysphoria in terms of differing qualities of sadness or, in other words, differing levels of mentalization of sadness. We conceptualize BPD as presenting both high levels of poorly mentalized sadness and low levels of well-mentalized sadness. Stated differently, a greater experience of psychic pain at a psychic equivalence level, combined with a lack of mentalized or mentalizing sadness (or a lack of adaptive sadness, or the capacity to mourn). To test this hypothesis, 208 undergraduate students (mean age: 22.4; 87.5% female) completed two online questionnaires: the Forms of Sadness Questionnaire (FSQ) and the McLean Screening Instrument for BPD (MSI-BPD). The FSQ assesses two forms/qualities, or levels of mentalization, of sadness: pathological sadness (here, self-critical sadness) and adaptive sadness (here, tolerated sadness). Correlations and hierarchical regression analysis were conducted. Correlations indicate that the number of self-reported BPD criteria is more strongly associated with self-critical sadness (pathological or less mentalized sadness: r = 0.51**, large effect size) and also, but less strongly, with tolerated sadness (adaptive or mentalized sadness: r = –0.35**, medium effect size). A hierarchical regression analysis was computed in order to assess the unique contribution of pathological and adaptive sadness on the prediction of BPD traits, over and above the contribution of the potential confounding variables of sex and age. Results indicate that both forms of sadness, as well as age, predict self-reported BPD traits. The strongest predictor is pathological, or less mentalized, sadness. The findings give an indirect indication that BPD dysphoria is composed of at least two types of subjective “depressive” or dysphoric experiences: mostly a type of suffering or sadness that is subjectively recognized through the perception of difficult to tolerate forms of mental pain; and also, a lack of a form of subjectively tolerable sadness, felt as productive. Since adaptive sadness contributes to BPD traits while the contribution of pathological sadness is statistically removed, the findings indicate that adaptive sadness is not reducible to pathological sadness, that it is not simply the reverse or absence of pathological sadness. This observation is even more significant when it is underscored that both forms of sadness are felt as at least somewhat dysphoric (both involve feeling “intense sadness”), reinforcing the idea that sadness can be an adaptative, although quite painful, experience. A clinical implication of these results might be that psychotherapy for individuals experiencing BPD symptoms should aim not only to reduce distress but also to cultivate a capacity to tolerate sadness in order to “suffer better” and mourn interpersonal losses.

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)0.009
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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.349
Teacher spread0.327 · 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".

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Citations0
Published2025
Admission routes2
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