Predicting Depressive and Anxiety Symptoms Among Lebanese and Syrian adults in a Suburb of Beirut during the Concurrent Crises: A Population-Based Study
Bibliographic record
Abstract
Abstract Background People living in low socioeconomic conditions are more prone to depression and anxiety. This study aimed to develop and internally validate prediction models for depressive and anxiety symptoms in Lebanese adults and Syrian refugees residing in a suburb of Beirut, Lebanon. Methods This was a population-based study among COVID-19 vulnerable adults in low socioeconomic neighborhoods in Sin-El-Fil, Lebanon. Data were collected through a telephone survey between June and October 2022. The outcomes depressive and anxiety symptoms were investigated for Lebanese and Syrian populations. Depressive and anxiety symptoms were defined as having a PHQ-9 and GAD-7 score of 10 or more respectively. Outcomes’ predictors were identified through LASSO regression, discrimination and model calibrations were assessed using area under curve (AUC) and C-Slope. Results Of 2,045 participants, 1,322 were Lebanese, 664 were Syrian, and 59 were from other nationalities. Among Lebanese and Syrian populations, 25.3% and 43.9% had depressive symptoms, respectively. Additional predictors for depressive symptoms were not attending school, not feeling safe at all at home, and not having someone to count on in times of difficulty. Not having legal residency documentation for Syrian adults was a context-specific predictor for depressive symptoms. These predictors were similar to that of anxiety symptoms. Both Lebanese and Syrian models had good discriminations and excellent calibrations. Conclusion This study highlights the main predictors of poor mental health were financial, health, and social indicators for both Lebanese and Syrian adults during the concurrent crisis in Lebanon. Findings emphasise social protection and financial support are required in populations with low socioeconomic status. Research in context What is already known on this topic The prevalence of depression and anxiety has increased globally. Vulnerable populations, such as refugees and populations of low socioeconomic status, are more prone to depression and anxiety. What this study adds This study included Lebanese and Syrian adults residing in low socioeconomic status areas of Sin-El-Fil, Lebanon. This is a population-based comparison of the predictors to poor mental health in Lebanon between refugees and Lebanese. The study highlights the need to meet financial, physical, and social needs of individuals to address mental health. How this study might affect research, practice, or policy The findings of this study highlight the need to reduce financial stress, address physical pain and social isolation, and advocate for Syrian residency documentation to reduce the occurrence of anxiety and depressive symptoms in people living in low socioeconomic conditions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".