Estudio sobre la relación entre fortalezas e indicadores de resiliencia durante el confinamiento debido a la nueva situación del covid-19 en estudiantes.
Bibliographic record
Abstract
El confinamiento forzado y prolongado puede tener consecuencias \nnegativas para la salud psicológica. La investigación ha mostrado que determinadas \nfortalezas se asocian a mayor resiliencia, pero no se ha analizado su relevancia en \nsituaciones de confinamiento. El objetivo de este estudio fue analizar la relación \nentre fortalezas e indicadores de resiliencia durante el confinamiento debido a la \nnueva situación del COVID-19. Participaron 131 estudiantes del grado de pedagogía \nde la Universidad de La Laguna. Se analizó la relación entre distintas fortalezas \n(Proyectos y Metas, Independencia, Optimismo, Orientación hacia el Futuro, \nAutodeterminación y Persistencia) y varios indicadores de resiliencia (Bienestar \nSubjetivo y Crecimiento Post- traumático). Los resultados confirmaron que un mayor \nnivel de fortalezas se asocia a un nivel más alto de Bienestar Subjetivo y \nCrecimiento Post-traumático. Se desprende la necesidad de indagar más sobre \nestas fortalezas para poder prevenir potenciales costos psicológicos.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".