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Record W2903177924 · doi:10.1521/pdps.2018.46.4.457

Addressing the Complexity of Perfectionism in Clinical Practice

2018· article· en· W2903177924 on OpenAlexaff
Joanna Cheek, David Kealy, Paul L. Hewitt, Samuel F. Mikail, Gordon L. Flett, Ariel Ko, Mary Jia

Bibliographic record

VenuePsychodynamic Psychiatry · 2018
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsYork UniversityUniversity of British Columbia
Fundersnot available
KeywordsPerfectionism (psychology)PsychologyConceptualizationInterpersonal communicationAnxietyPsychodynamicsPersonalityPsychotherapistClinical psychologyPersonality disordersConstruct (python library)PsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Perfectionism, defined as the need to be or appear to be perfect, is a multidimensional personality construct that makes individuals vulnerable to a host of clinical problems including depression, anxiety, personality and eating disorders, as well as suicide behaviors, interpersonal dysfunction, and difficulties achieving successful therapeutic outcome. Given the detrimental associations with perfectionism, it is crucial that mental health professionals be familiar with and able to identify patients presenting with perfectionistic characteristics. The purpose of this article is to provide an overview of a comprehensive conceptualization of perfectionism and its assessment and treatment in clinical practice based on the psychodynamic and interpersonal perspective of Hewitt et al. (2017). This article presents conceptual models of perfectionism, assessment measures, treatment considerations and challenges, and a case example of a patient with perfectionism. Through understanding the nature and treatment of perfectionism, clinicians can broaden and strengthen their knowledge and skill in helping patients struggling with perfectionistic difficulties and the attendant symptoms, syndromes, and disorders.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.349
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.165
GPT teacher head0.461
Teacher spread0.296 · 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.

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".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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