Measuring Emotional Awareness from a Cognitive-Developmental Perspective: Portuguese Adaptation Studies of the Levels of Emotional Awareness Scale
Bibliographic record
Abstract
INTRODUCTION: The Levels of Emotional Awareness Scale (LEAS) was developed to assess the emotional awareness construct, based on a cognitive-developmental perspective and influenced by the Piaget and Werner theories. It is composed of 20 emotion-evoking scenes and has been used in multiple researches related to emotion regulation, alexithymia and psychiatric disorders. It is a well-documented, valid and reliable measure. Due to the extent of LEAS, some investigators have been using one of the parallel forms (LEAS-A), which is a part of the complete version, nevertheless there is a gap of studies concerning LEAS-A psychometric qualities. In the absence of measures for assessing the organization of the emotional experience in Portuguese samples, we developed the Portuguese version of LEAS, characterizing reliability and validity indicators and the same for LEAS-A. MATERIALS AND METHODS: Three different studies were carried out with these versions, two with university students and another with a sample from the general population. RESULTS: The Portuguese version showed high levels of reliability, superior to those found in other adaptation procedures. LEAS-A showed good reliability and indicators of discriminant and concurrent validities. The LEAS-A scores were independent from negative affect and related to the externally-oriented thinking involved in alexithymia. CONCLUSIONS: The Portuguese LEAS and LEAS-A show very adequate qualities, which allow for their scientific use. Implications for clinical and research contexts are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".