Brazilian Version of the Protocole Montréal d'Évaluation de la Communication (Protocole MEC): Normative and Reliability Data
Bibliographic record
Abstract
The lack of standardized instruments to evaluate communication disorders related to the right hemisphere was verified. A new evaluation tool was developed: Protocole Montréal d'Évaluation de la Communication – Protocole MEC, adapted to Brazilian Portuguese – Bateria Montreal de Avaliação da Comunicação – Bateria MAC (Montreal Evaluation of Communication Battery). The purpose was to present stratified normative data by age and educational level, and to verify the reliability parameters of the MEC Battery. 300 individuals, between the ages of 19 and 75 years, and levels of formal education between 2 and 35 years, participated in this study. They were divided equally into six normative groups, according to three age categories (young adults, intermediary age, and seniors) and two educational levels (low and high). Two procedures were used to check reliability: Cronbach alpha and reliability between evaluators. Results were established at the 10th percentile, and an alert point per task for each normative group. Cronbach's alpha was, in general, between .70 and .90 and the average rate of agreement between evaluators varied from .62 to .94. Standards of age and education were established. The reliability of this instrument was verified. The psychometric legitimization of the MEC Battery will contribute to the diagnostic process for communicative disorders.
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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 | 0.052 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".