Mental health and risk-taking among refugees in Lebanon
Bibliographic record
Abstract
Lebanon is rapidly adapting public services meet local needs as well those of refugees from conflict regions such as Syria. However, these challenges are complicated by high volumes of individuals with poor mental health, who are also at risk of poor decision-making and may avoid use of health services due to low trust in government institutions, among other reasons.Over 700 individuals residing in Lebanon, including Syrian refugees and Palestinians from Lebanon, completed a series of measures covering decision-making with risk, mental health, and trust. Analyses focused on whether there were significant relationships between these three, and if various factors influencing those relationships could be useful for health policy in Lebanon and other regions affected by conflict-related population movement.This study finds that higher subjective well-being was associated with more risk-taking among refugees (β = 0·07, SE = 0·02, z = 4·63, p < ·01), but not among the Lebanese host population (β = -0·003, SE = 0·01, z = -0·32, p =·75). However, average subjective well-being did not significantly differ between the Lebanese host population and the refugees (absolute difference = -1·27, 95%CI = [-2·83, 0·29], on a 60-point scale), or between Syrian and Palestinian refugees (absolute difference = -1·53, 95%CI = [-4·16, 1·08]). There were moderate effects of behavioral interventions on risk-taking in terms of increased advantageous choices.There is a clear pattern of greater risk-taking for refugees with better subjective well-being. This is an important finding as greater risk-taking is associated with a number of negative health outcomes, particularly in vulnerable populations. While the behavioural interventions do show some effect on improving advantageous choice, these risk patterns are of clear interest to policymakers dealing with health and well-being of all residents in Lebanon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".