Análisis psicométrico del inventario de síntomas prefrontales abreviado: evidencias de su validez y fiabilidad en la población general venezolana
Bibliographic record
Abstract
INTRODUCTION: Few tools exist to objectively measure dysfunctions of prefrontal origin self-reported by the general population. The Prefrontal Symptom Inventory (PSI) is a test with excellent psychometric properties that allows such assessment and so far, no robust analysis of its abbreviated version in Spanish for Latin America has been performed. AIMS: To analyze the psychometric properties of the abbreviated PSI in terms of reliability and validity in the general population in the Venezuelan context. SUBJECTS AND METHODS: 300 subjects from the general population participated. The factor structure of the abbreviated ISP was determined through confirmatory factor analysis (CFA); construct validity was assessed by contrasting groups with no risk of MCI and the convergence of scores with the domains that make up the Montreal Cognitive Assessment (MoCA). Likewise, internal consistency was estimated through McDonald's ? and Cronbach's a. RESULTS: Five factorial models were contrasted and a version of the PSI composed of 18 items was obtained, which presented excellent indicators of goodness of fit (?2 (132) = 200.057, p < 0.001, CFI=0.955, TLI=0.948, SRMR=0.042, RMSEA=0.041) and internal consistency (? = 0.90; a = 0.89). Likewise, statistically significant differences between groups and inverse correlations were evidenced with the sections evaluated in the MoCA except for abstraction. CONCLUSION: The PSI-18 is a valid and reliable measure to be used in the studied population. Consistently, previous studies show its versatility to be used in research and health contexts.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".