Factores de riesgo asociados a ansiedad y depresión por COVID-19, una revisión sistémica
Bibliographic record
Abstract
Estudiar los factores que inciden en los trastornos mentales es crucial para comprender mejor su etiología, desarrollar tratamientos efectivos y promover la salud en la sociedad. El objetivo de la investigación fue describir el estado de las investigaciones sobre los factores de riesgo asociados a la COVID-19 y su impacto en la aparición de ansiedad y depresión en diversos sectores de la población. Se realizó una revisión sistemática, seleccionándose 20 artículos comprendidos entre 2020 y 2024, usando el método PRISMA. Los resultados indicaron que el factor más prevalente es la atención a pacientes contagiados, seguido de sexo, edad, las condiciones sociodemográficas, los antecedentes de salud mental, comorbilidades, número de hijos, condiciones de confinamiento, educación virtual, estado civil y preocupación por la salud. Estos resultados subrayan la necesidad urgente de desarrollar estrategias de prevención e intervención que aborden los factores de riesgo identificados, con el fin de mitigar el impacto psicológico de la pandemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".