The Assessment of an Extended Set of Socio-Economic Determinants to Explain Anxiety and Uncertainty, Insufficient Quality and Food Intake of Afghan Refugees
Bibliographic record
Abstract
OBJECTIVES: In this study, socio-economic factors associated with Afghan refugee households' food insecurity, anxiety and uncertainty, insufficient quality and food intake were determined. DESIGN: Household Food Insecurity Assess Scale measurement was applied to assess food insecurity, anxiety and uncertainty, insufficient quality and insufficient food intake. Descriptive analysis and multivariable regression models were used to determine the associated factors. SETTING: The study was carried out in urban areas of Tehran province in Iran. PARTICIPANTS: To collect data, interviews were conducted among 317 Afghan households. The questionnaire was administered via face-to-face interviews to either the breadwinner of the selected households or a member who could respond on behalf of the household. RESULTS: About 11·3 % of Afghan households who resettled in Tehran province were food secure, while 11·7 % were marginally, 40·7 % moderately and 36·3 % severely food insecure. Economic and financial factors were inversely and significantly associated with food insecurity. Employment, income, distance from the central market and personal saving were inversely associated with food insecurity, while other determinants, including the length of living time in Tehran, house type and the number of male and female children, had a direct association with food insecurity. CONCLUSIONS: The associations of socio-economic factors with three categories of food insecurity differed. Elimination of occupation bans that the Iranian government imposes on refugees provides simple access to financial supports like long-term loans, and opening a bank account for refugees will benefit both Iranians and refugees.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".