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Record W4392543002 · doi:10.1097/hrp.0000000000000391

Readiness and Personality Disorders: Considering Patients’ Readiness for Change and Our System’s Readiness for Patients

2024· article· en· W4392543002 on OpenAlexaff
Connor Hawkins, David Kealy

Bibliographic record

VenueHarvard Review of Psychiatry · 2024
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaSmiths Detection (Canada)Vancouver Coastal Health
Fundersnot available
KeywordsPersonality disordersPersonalityContext (archaeology)PsychologyClinical psychologyPsychiatryPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT: The culture around personality disorder treatment has changed drastically in the past generation. While once perceived as effectively untreatable, there are now numerous evidence-based treatment approaches for personality disorders (especially borderline personality disorder). The questions, however, of who should be matched to which treatment approach, and when, remain largely unanswered. In other areas of psychiatry, particularly substance use disorders and eating disorders, assessing patient treatment readiness is viewed as indispensable for treatment planning. Despite this, relatively little research has been done with respect to readiness and personality disorder treatment. In this article, we propose multiple explanations for why this may be the case, relating to both the unique features of personality disorders and the current cultural landscape around their treatment. While patients with personality disorders often face cruel stigmatization, and much more work needs to be done to expand access to care (i.e., our system's readiness for patients), even gold-standard treatment options are unlikely to work if a patient is not ready for treatment. Further study of readiness in the context of personality disorders could help more effectively match patients to the right treatment, at the right time. Such research could also aid development of strategies to enhance patient readiness.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.036
GPT teacher head0.331
Teacher spread0.295 · 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 teacher head, not a consensus.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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