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Record W4385879749 · doi:10.1027/2698-1866/a000050

Psychometric Properties of the Brazilian Portuguese Version of the Circumplex Scales of Interpersonal Problems (CSIP)

2023· article· en· W4385879749 on OpenAlexaff
Vinícius Betzel Koehler, Michael J. Boudreaux, Rosana Suemi Tokumaru

Bibliographic record

VenuePsychological Test Adaptation and Development · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInterpersonal communicationPortugueseConstruct (python library)ChecklistConstruct validityScale (ratio)Concurrent validityPsychometricsInterpersonal relationshipPsychologyClinical psychologySocial psychologyComputer scienceGeographyCognitive psychologyInternal consistencyCartography

Abstract

fetched live from OpenAlex

Abstract. The Circumplex Scales of Interpersonal Problems (CSIP) was developed in American English to assess maladaptive variants of the interpersonal circumplex. In this article, we describe the psychometric properties and construct validity of a Brazilian Portuguese version of the CSIP using data from two samples of Brazilian adults. The results from exploratory and confirmatory structural analyses indicated strong support for a circumplex representation of the CSIP in both samples. Scores on the CSIP converged with the Brazilian Portuguese version of the Checklist of Interpersonal Transactions – Revised and demonstrated test–retest stability over 8 months. Taken as a whole, the results extend support for the circumplex structure, construct validity, and retest reliability of the CSIP and indicate that the Brazilian Portuguese translation is psychometrically adequate to investigate interpersonal problems in Brazilian populations.

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)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.088
GPT teacher head0.293
Teacher spread0.204 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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