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Record W4387216554 · doi:10.1177/19485506231191084

Personality and Lifetime Need Frustration: A Person-Centered Perspective on Interpersonal Problems and Personality Pathology

2023· article· en· W4387216554 on OpenAlexafffund
Chris Sciberas, Marc A. Fournier

Bibliographic record

VenueSocial Psychological and Personality Science · 2023
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPersonality pathologyFrustrationInterpersonal communicationPersonalityTraitPerspective (graphical)Social psychologyBig Five personality traitsDistressInterpersonal relationshipClinical psychologyPersonality disorders

Abstract

fetched live from OpenAlex

The current research adopted a person-centered approach to examine whether people’s experiences of lifetime need frustration interact with their personality trait profiles to predict their problems and pathology from the perspectives of both the interpersonal circumplex (IPC) and the five-factor model (FFM). Data ( N = 1,026) were analyzed using multilevel modeling. Consistent with prediction, lifetime need frustration predicted participants’ overall levels of interpersonal distress and personality pathology. Furthermore, levels of lifetime need frustration predicted the strength of the relationship between participants’ trait profiles (i.e., IPC and FFM) and their corresponding profiles of interpersonal problems and personality pathology. Findings from the present study demonstrate how between-person differences in lifetime need frustration give rise to the within-person organization of psychological maladjustment and highlight the importance of people’s traits in predicting their unique maladaptations to having their basic psychological needs frustrated.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.380
Teacher spread0.271 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2023
Admission routes2
Has abstractyes

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