Evidencias preliminares de validez de la Escalade Trastorno Depresivo Persistente (TDP) en estudiantes universitarios peruanos
Bibliographic record
Abstract
Persistent Depressive Disorder (PDD), also known as dysthymia, has a chronic course and a negative impact on people's quality of life, even more so in young university students. This disorder is one of the least studied types of mood disorders and lacks instruments for its proper evaluation. Therefore, the main objective of the study was to build a scale to measure PDD and preliminarily evaluate its psychometric properties. The total sample of the study was 868 university students aged 18 to 34 years. The results of the exploratory factor analysis showed the presence of a single factor. Likewise, the confirmatory factor analysis reported adequate goodness-of-fit indices (CFI = .98, TLI = .97, SRMR = .06, RMSEA [90%CI] = .09 [.08 -.10]) for a single-factor structure. In addition, the scale proved to be invariant according to the sex of the participants. Regarding convergent validity, PDD showed significant covariances with anxiety, depression, and life satisfaction. Finally, the scale showed high reliability (α = .88; ω = .88). In conclusion, the PDD scale preliminarily presents adequate psychometric properties to measure the presence of patterns of persistent depressive disorder in university students.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".