General Health of Foreign-Origin Groups and Native Population
Bibliographic record
Abstract
BACKGROUND: Since the mental health of marginal settlers (non-native population) may affect other citizens' health, the present study attempts to investigate the mental health status of marginal settlers of Yazd. MATERIALS & METHODS: this study was a descriptive, cross-sectional research, in which 400 of non-native and native population have participated. To study mental health status of people, a questionnaire was used. The first section of this questionnaire was the 28-item questionnaire of GHQ and the second section dealt with demographic characteristics such as age, sex, employment status, household income, and educational level of the father of the family. The collected data was analyzed using statistical operations of Pearson correlation coefficient, T Student, univariate Anova, and non-parametric Chi Square. RESULTS: The results revealed that the average scores of general health were 20.09±9.84 and 17.04±9.54 for native and non-native population, respectively. Among subscales of general health, the highest and lowest average scores belonged to social dysfunctions, which showed a dangerous mental health status, and depression, respectively. There was significant difference between average score of general health and educational level of the father of the family (p<.001). The temporary employment and leased household differs significantly from the average score of general health among native population. It was indicated that sex was one of the most powerful predictors of mental health and people had more mental health when they grew older. Anxiety was the strongest predictor of general health for both groups. CONCLUSION: It seems that background factors such as educational level and employment status effect general health of people more than living in marginal settlement.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".