Mood and Anxiety Disorders and the Use of Services and Psychotropic Medication in an Immigrant Population: Findings from the Israel National Health Survey
Bibliographic record
Abstract
OBJECTIVE: Using the Israel National Health Survey (INHS), we compared immigrants' 12-month prevalence of mental disorders and the use of services and psychotropic drugs with that of the general population. METHODS: A representative sample of noninstitutionalized residents, aged 21 years and older, was drawn from the National Population Register. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) disorders were assessed using a revised version of the Composite International Diagnostic Interview. Respondents were asked to report any health service and psychotropic drug use in the past 12 months. RESULTS: During the 12 months preceding the INHS, immigrants and Israelis (that is, those born in Israel or those who emigrated to Israel before 1989) were equally likely to have a common mental disorder (OR 0.9; 95% CI 0.7 to 1.1) and to use health services (OR 0.9; 95% CI 0.7 to 1.2). However, among respondents who did not meet the DSM-IV criteria for a specific mental disorder, the immigrants reported markedly more use of psychotropic drugs than the Israelis, in particular more anxiolytics, mood stabilizers, and hypnotics. CONCLUSION: The results suggest that the common mental disorders and mental health service use among the immigrants are no higher than that among their Israeli counterparts. The higher use of psychotropic drugs by immigrants may be an indirect indicator of a higher level of psychological distress symptoms, such as anxiety, depression, and sleep disorders.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".