Severe Anxiety, Stress, and Depression according to Life Satisfaction among Residents of Latin America
Bibliographic record
Abstract
Introduction: Severe anxiety, stress, and depression can cause a significant problem, which affects the response to everyday situations and has an impact on life satisfaction; however, there are no published studies that evaluate this situation in Latin America. The aim of the study was to evaluate the association between severe anxiety, stress, and depression according to life satisfaction in Latin American residents. Methods: A cross-sectional, analytical, and multicenter study was conducted in Latin American countries, analyzing a database of people surveyed virtually. Anxiety, depression, and stress were measured with the DASS-21 test (Cronbach’s alpha: 0.97) and life satisfaction with the SWLS test (Cronbach’s alpha: 0.89). Descriptive and analytical statistics were obtained. Results: Of 2,002 respondents, 28% presented dissatisfaction with life, of which 34%, 25%, and 19% suffered from anxiety, depression, and anxiety in severe degrees, respectively. In the multivariate analysis, greater dissatisfaction with life was found in people with severe degrees of depression (PRa: 4.22; 95% CI: 3.14–5.67; p value <0.001), anxiety (PRa: 2.25; 95% CI: 2.04–2.48; p value <0.001), and stress (PRa: 2.77; 95% CI: 2.27–3.37; p value <0.001). Conclusion: The three severe states showed significant statistical correlations with life dissatisfaction, a factor that had not been previously measured in such a large population following the pandemic a few years ago. Health institutions in each country must consider this.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".