Ethnic and Gender Differences in Mental Health Utilization: the Case of Muslim Jordanian and Moroccan Jewish Israeli Out-Patient Psychiatric Patients
Bibliographic record
Abstract
A sample of 148 (87 Jordanian [61 male, 26 female] and 61 Israeli [26 male, 35 female]) was selected from a psychiatric clinic in Ashdod Israel and Zarka Jordan, using convenience sampling methodology over a 12 month period in late 1997 and early 1998. A revised Hopkins Symptom Checklist: A Self-Report Symptom Inventory (HSCL) was translated into Arabic and Hebrew and distributed to subjects; additional questions explored demographic characteristics, forms of received treatment, patient perceptions of treatment efficacy, patient use of traditional healers, and patient explanation of etiology. Data revealed that there were differences in dimensions between the 2 groups based on nationality and gender. More Jordanians than Israelis expected medications as the main treatment, and unlike Israelis, no Jordanian patients received individual psychotherapy. Israelis expected medications, advice, directions, and instructions from psychiatrists. Both ethnic groups consulted a wide array of traditional healers, although precise types of healers varied according to gender and ethnicity. Israeli subjects gave more diverse explanations of mental health etiologies: physical, family, divorce, economic, unemployment; whereas Jordanians tended to emphasize divine and spiritual sources. Implications for psychiatric practice are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".