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Record W4294339255 · doi:10.1177/17456916221100464

Destigmatizing Borderline Personality Disorder: A Call to Action for Psychological Science

2022· review· en· W4294339255 on OpenAlexaff
Sara R. Masland, Sarah E. Victor, Jessica R. Peters, Skye Fitzpatrick, Katherine L. Dixon–Gordon, Alexandra H. Bettis, Kellyann M. Navarre, Shireen L. Rizvi

Bibliographic record

VenuePerspectives on Psychological Science · 2022
Typereview
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsYork University
Fundersnot available
KeywordsPejorativeBorderline personality disorderCall to actionStigma (botany)PsychologyPsychological scienceAction (physics)Clinical psychologyPsychotherapistPsychiatrySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Despite recognition that borderline personality disorder (BPD) is one of the most stigmatized psychological disorders, destigmatization efforts have thus far focused on the views and actions of clinicians and the general public, neglecting the critical role that psychological science plays in perpetuating or mitigating stigma. This article was catalyzed by recent concerns about how research and editorial processes propagate stigma and thereby fail people with BPD and the scientists who study BPD. We provide a brief overview of the BPD diagnosis and its history. We then review how BPD has been stigmatized in psychological science, the gendered nature of BPD stigma, and the consequences of this stigmatization. Finally, we offer specific recommendations for researchers, reviewers, and editors who wish to use science to advance our understanding of BPD without perpetuating pejorative views of the disorder. These recommendations constitute a call to action to use psychological science in the service of the public good.

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)0.020
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0020.006
Scholarly communication0.0070.014
Open science0.0020.002
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0030.001

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 head0.185
GPT teacher head0.516
Teacher spread0.331 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations35
Published2022
Admission routes1
Has abstractyes

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