Prevalence of post-traumatic stress disorder risk post-COVID-19 in 12 countries in Latin America: a cross-sectional survey
Bibliographic record
Abstract
Introduction Latin America was the region most affected by COVID-19 in the second quarter of 2020, and consequently, the impact on mental health requires evaluation. The aim of this study was to assess the risk of post-traumatic stress disorder (PTSD) caused by bereavement due to COVID-19 in 12 countries in Latin America. Methods The current study was an analytical cross-sectional study. Validated tests were applied for PTSD, depression, anxiety, and stress (DASS-21), questions about the respondent’s condition or their environment, and demographic questions, as well as the length of the mourning period of suffering. Results The outcomes demonstrated that the PTSD risk increased for women (p < 0.001), when a friend or acquaintance had COVID-19 (p = 0.002), when a close relative died from COVID-19 (p = 0.010), having severe depression (p <0.001), severe anxiety (p <0.001), severe stress (p <0.001), residing in Chile (p <0.001), Paraguay (p <0.001), Bolivia (p <0.001), Costa Rica (p <0.001) or El Salvador (p = 0.005). On the other hand, there was less risk of PTSD at an older age (p <0.001) or if respondents had a sentimental partner (p = 0.025). In the case of severe PTSD, there was a greater gender risk for women (p <0.001), a close relative dying from COVID-19 (p = 0.017), having severe depression (p <0.001), severe anxiety (p <0.001), severe stress (p <0.001), residing in Chile (p <0.001), Paraguay (p <0.001), Bolivia (p <0.001) and Costa Rica (p = 0.002). It was also observed that there was less risk of severe PTSD at an older age demographic (p <0.001). Discussion It can be concluded that the percentages of PTSD are high in its clinical presentation as severe, especially among Latin American women.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".