Mental health and decisions under risk among refugees and the public in Lebanon
Bibliographic record
Abstract
Abstract Lebanon is rapidly adapting public services to meet local needs as well as 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 the use of health services due to low trust in government institutions, among other reasons. Over 700 individuals residing in Lebanon, including Lebanese nationals, Syrian refugees and Palestinians from Lebanon, completed a series of measures covering decision-making with risk, mental health, and trust. The aim was to determine if significant relationships existed between these three and if those patterns were consistent between the three populations. A widely used well-being questionnaire produced similar unidimensional factor structures as found in other settings, indicating suitability for use in Lebanon, including refugees. Higher subjective well-being was associated with more risk-taking among refugees ( β = 0.07, SE = 0.02, z = 4.63, p < 0.01), but not among the Lebanese host population ( β = −0.003, SE = 0.01, z = −0.32, p = 0.75). However, average subjective well-being did not significantly differ between the Lebanese host population and 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]). Behavioural interventions (nudges and boosts) designed to support people in making choices more advantageous for them showed moderate effects. 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 can be 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 the health and well-being of all residents in Lebanon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".