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Record W4387490607 · doi:10.22235/cp.v17i2.2881

Adaptation and validation of the Psychologist and Counsellor Self-Efficacy Scale (PCES) among Brazilian psychologists and psychology students

2023· article· en· W4387490607 on OpenAlexfundno aff
Suzanna Araújo Preuhs, Gabriel Teixeira Da Silva, Andressa Melina Becker da Silva, Fernanda Machado Lopes, André Luiz Monezi Andrade

Bibliographic record

VenueCiencias Psicológicas · 2023
Typearticle
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of Toronto
KeywordsPsychologyScale (ratio)Context (archaeology)Construct validityConvergent validityConfirmatory factor analysisApplied psychologyClinical psychologyReliability (semiconductor)PsychometricsStructural equation modelingComputer science

Abstract

fetched live from OpenAlex

This study aimed to: (i) Translate, adapt, and evaluate the linguistic and semantic properties of the Psychologist and Counsellor Self-Efficacy Scale (PCES) for use among psychology students and psychologists in Brazil; (ii) Assess the psychometric attributes of the adapted PCES, including its construct validity and other related psychometric properties. A total of 2,139 participants, comprising psychologists and psychology students (Mage = 25.36 years; SD = 9.46), partook in this study. The PCES exhibited commendable fit indices (CFI = .990; TLI = .989; SMR = .043), and multigroup confirmatory factor analysis revealed a consistent level of invariance between students and practicing psychologists, as well as between male and female participants. Network analysis offered significant insights into the distribution of factors, while the convergent validity of PCES was corroborated by its correlations with the subscales of the General Perceived Self-Efficacy Scale (GPSS). These findings unequivocally affirm the reliability and suitability of PCES as an assessment instrument for both psychologists and psychology students in the Brazilian context.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.058
GPT teacher head0.395
Teacher spread0.337 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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