Impactos de la pandemia de COVID-19 en la salud mental de adolescentes uruguayos
Bibliographic record
Abstract
Objectives: This study reports levels of wellbeing and symptoms of anxiety, depression, posttraumatic stress, sleep disturbances, as well as the incidence of self-harm behaviours, suicidal ideation, suicide attempts among Uruguayan adolescents during the COVID-19 pandemic, by comparing self-report data corresponding to the times before and during the pandemic across gender and prior diagnosis of mental health disorder. Method: We conducted a cross-sectional, observational study using data from the international COH-FIT survey, involving 282 Uruguayan adolescents aged 14-17 (56.4% female), who were recruited through convenience sampling. Results: We observed an overall increase in self-reported symptoms of mental disorders and a decrease in wellbeing among adolescents. In addition, a high number of adolescents reported non-suicidal self-harm (25.9%), suicidal ideation (38.6%) and suicide attempts (6.7%). Regarding subgroup analyses, female adolescents and adolescents with a prior diagnosis of a mental health disorder presented lower levels of self-reported wellbeing, higher levels of symptoms of mental disorders, and a higher frequency of self-harm behaviours and suicidal ideation. Conclusion: We observed a deterioration in wellbeing and symptoms of mental disorders during the COVID-19 pandemic, being the high frequency of suicidal ideation of particular concern.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".