Salud mental en estudiantes de educación superior en Chile: una revisión de alcance con meta-análisis
Bibliographic record
Abstract
Realizamos una revisión de alcance sobre la prevalencia de síntomas psicológicos, factores asociados a la salud mental, barreras y facilitadores para la búsqueda de ayuda, y la efectividad de intervenciones de salud mental en estudiantes de educación superior en Chile.Buscamos reportes indexados hasta el 15 de octubre de 2019 en las bases de datos CINAHL, Google Scholar, PubMed, Virtual Health Library/BIREME y Web of Science.Evaluamos la calidad de los estudios de prevalencia y exploramos la prevalencia combinada de síntomas psicológicos mediante meta-análisis.Incluimos 32 estudios publicados, 20 sobre prevalencia de síntomas -10 con la calidad suficiente para ser incluidos en el meta-análisis.Observamos una heterogeneidad sustantiva en la exploración de prevalencia combinada de síntomas, con rangos entre 22,9% a 40,7% para malestar psicológico, 16,5% a 38,8% para síntomas depresivos, 16,5% a 23,7% para síntomas ansiosos, 19,7% a 29,7% para consumo de cannabis en los últimos 12 meses, y 84,0% a 92,6% para consumo de alcohol en los últimos 12 meses.El sexo femenino se asoció consistentemente con problemas de salud mental.Es necesario realizar más estudios que evalúen el acceso a tratamiento, facilitadores y barreras para la búsqueda de ayuda, e intervenciones para mejorar la salud mental de la población objetivo.
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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 | 0.035 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".