Mental Health in Arab Americans: Prevention and Interventions
Bibliographic record
Abstract
Arab Americans and others of Middle Eastern descent share health concerns with others who have personal or collective histories of trauma. For this reason, it is important to consider the various theories and models that have been utilized in the effort to better understand how to foster mental health and mental illness prevention and to effectively intervene when and where necessary. In this chapter, we address relevant historical and gender issues, ongoing global mental health initiatives, and the ethical standards that must be maintained to both conduct research and effectively engage in clinical practice with Americans of Arab and/or Middle Eastern ethnic backgrounds. School psychology holds promise for one way to implement prevention and intervention measures with youth. School psychologists as well as researchers and other clinicians working with individuals and families need to be attuned to how best to conduct culturally sensitive and competent assessments and interventions while also maintaining an active stance in cultural humility. Interventions at the community level including social services and prevention programs are highlighted in the chapter along with other forms and modalities of intervention such as telepsychology, cognitive behavioral therapy, brief narrative exposure therapy, trauma therapy, and family therapy. The need for the use of evidence-based mental health interventions with Americans of Arab and/or Middle Eastern ethnicity needs to be balanced with the need for local, culturally meaningful adaptations including during crises such as the COVID-19 pandemic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".