Cross-validation of the demoralization construct in the Revised NEO Personality Inventory.
Bibliographic record
Abstract
Demoralization is defined as a pervasive, generalized negative emotional construct present in psychiatric disorders and a variety of medical conditions. Demoralization is also conceptualized as a ubiquitous affective-laden factor common to most forms of psychopathology that increases the magnitude of intercorrelations among putatively distinct psychiatric symptom scales (Tellegen, 1985). Using exploratory structural equation modeling to identify common variance across the revised NEO Personality Inventory (NEO PI-R), a measure of the five-factor model of personality, Noordhof, Sellbom, Eigenhuis, and Kamphuis (2015) constructed an 18-item Demoralization subscale in a Dutch-speaking sample of patients attending a clinic for personality disorders in the Netherlands. In the current study we sought to cross-validate these findings in an English-speaking and diagnostically heterogeneous sample of psychiatric patients (N = 1930) receiving consultation or treatment at a large mental health and addiction center in Canada. Our results support the construct validity of the Demoralization subscale and its capacity to account for demoralization-related variance in the NEO PI-R. We believe these findings support the general tenets of demoralization and the presence of this construct in the NEO PI-R item pool. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".