A Severe and Chronic Socio-Economic Crisis with no End in Sight, How Much Can Lebanese People Take?
Bibliographic record
Abstract
Abstract Purpose People in Lebanon have been facing what has been dubbed one of the most severe socio-economic crises since the 21st century. Over the past few years, suicide rates have spiked, fueled by deteriorating working and living conditions. Research conducted on the mental health of populations residing in low-income countries is meager. Amongst these countries, very little research has examined the repercussions of a severe socio-economic crisis on Lebanese people's mental health. Methods For the purpose of this study, we conducted a multiple regression analysis. The multiple regression analysis allowed us to verify the association between several independent variables (work-related, macro-economic variables, life stressors and family income) and our dependent variables (stress and psychological distress). We also ran mediation analysis to examine the indirect role our variables could play on psychological distress via their effect on stress. Results Through a community sample of workers, lack of access to electricity due to the crisis was found to be associated with stress. Similarly, stress related to the crisis, deteriorating working conditions, job insecurity, lack of internet access, and other life stressors were also found to be associated with a higher level of psychological distress. Higher family income and recognition at work seemed to be associated with a lower level of psychological distress. Lastly, lack of access to electricity seemed to be positively associated with psychological distress via stress related to the socio-economic crisis. Conclusion The impact of the current situation could have broad implications for a large number of low-income countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".