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Record W2906500265 · doi:10.1080/15332985.2018.1555104

A systematic literature review of the etiology of borderline personality disorder from an ecological systems PERSPECTIVE

2018· article· en· W2906500265 on OpenAlexaff
Aman Ahluwalia Cameron, Kimberly A. Calderwood, Suzanne McMurphy

Bibliographic record

VenueSocial Work in Mental Health · 2018
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsTrent UniversityUniversity of Windsor
Fundersnot available
KeywordsPerspective (graphical)Borderline personality disorderEtiologyPsychologyPersonalityClinical psychologyEcologyPsychotherapistPsychiatryPsychoanalysisBiology

Abstract

fetched live from OpenAlex

Borderline Personality Disorder (BPD) is one of the most common, complex, costly, and severely impairing personality disorders, affecting an estimated 2% to 9% of the general population and 40% to 44% of the inpatient psychiatric population. A review of the literature was conducted using a systematic methodology. By incorporating an ecological systems perspective, a holistic and comprehensive critique of the literature surrounding the etiology of BPD is presented. The findings reveal that the etiology of BPD is a complex integration of psychological, biological, and social factors. More specifically, however, this review found that: (1) the etiology of BPD is complex and has many factors, (2) the dominant discourse about the etiology of BPD is based primarily in the psychological and biological literature, and (3) the examination of BPD etiology has focused solely on the individual and microsystems levels, neglecting to consider systemic factors such as the impact of discriminatory health and mental healthcare practices. Findings and future directions are explored through the ecological systems theory lens.

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)0.008
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0240.022
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.396
Teacher spread0.373 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2018
Admission routes1
Has abstractyes

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