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Record W4403523096 · doi:10.22491/1678-4669.20220002

Adaptation and psychometric evaluation of a brazilian version of the CYRM-28

2023· article· en· W4403523096 on OpenAlexaff
Aurino Lima Ferreira, Marlos Alves Bezerra, Leonardo Xavier de Lima e Silva, Philip Jefferies, Renata Maria Coimbra, Djailton Pereira da Cunha, Andreza Souza Santos, Maria Lúcia Ferreira da Silva, Tatiana Lima Brasil, Michael Ungar

Bibliographic record

VenueEstudos de Psicologia (Natal) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth Education and Validation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAdaptation (eye)PsychologyCognitive psychologyClinical psychologyNeuroscience

Abstract

fetched live from OpenAlex

Using a sample of 832 young people, between 13 and 25 years old, the present research examined the psychometric properties of a Brazilian adaptation of the Child and Youth Resilience Measure-28 (CYRM-28), a scale empirically derived from a threefactor resilience model that has been promising for cross-cultural research. To establish validation, we use Confirmatory Factor Analysis to determine whether the traditional three-factor structure and the original items of CYRM-28 are compatible with a Brazilian sample. This was followed by tests of internal consistency by examining Cronbach’s alpha and convergent validity by testing correlations with the CD-RISC-10. The results led to a reduced version of 19 items distributed in three resilience factors. The findings are consistent with those observed in samples from other cultures and suggest that CYRM-19-Br is promising for use in resilience research in Brazil.

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)0.011
metaresearch head score (Gemma)0.026
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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.026
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 science0.0010.002
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 head0.160
GPT teacher head0.429
Teacher spread0.269 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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