PREVALENCE OF DEPRESSION AND ANXIETY IN COLOMBIA: WHAT HAPPENED DURING COVID-19 PANDEMIC?
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has impacted the well-being of millions of people around the globe. During the COVID-19 pandemic, the mental health of the population was affected, which means that governments would need to implement different actions to mitigate and treat mental health disorders result of the pandemic. This study aims to estimate the prevalence of anxiety and depression for female and male adolescents and adults in Colombia before the COVID-19 pandemic. It also aimed to estimate the potential increase of the prevalence in each group as a result of the COVID-19 pandemic in 2020. We used the Individual Registry of Health Services Delivery data from 2015 - 2021 to estimate the observed prevalence of anxiety and depression. Using the National Mental Health Survey 2015, we simulated the expected prevalence of anxiety and depression for adolescents (12 to 17 years) and adults (18 or older) from 2016 to 2020. We used an arithmetic static Monte Carlo simulation process to estimate the expected prevalence. The results of the analysis using revealed an important increase in the observed prevalence of these disorders for adults and adolescents and men and women between 2015 and February 2020. When we simulated different scenarios using the National Mental Health Survey and estimated the prevalence of both depression and anxiety for adults and adolescents, we found that the prevalence of depression and anxiety has had an important increase in the last five years for all groups and had an important increase during the 2020. This increase has been greater for women than for men, and for adolescents than adults. Our results show the number of people who need potential attention from the health system in Colombia and highlight the importance to think about how to avoid and detect potential cases of anxiety and depression especially in female adolescents.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".