A Predictive Model of Apartment-Living Based on Socio-Economic and Demographic Factors With Health-Based Approach in Iran
Bibliographic record
Abstract
OBJECTIVE: Due to importance and progressive growth of apartment-living phenomenon in the world today, it is essential to survey the different dimensions of this modern lifestyle. The aim of this study is to predict rate of apartment-living based on the different predicted variables of socio-economic and demographic factors with approach to different health aspects. METHODS: A descriptive- analytic study was carried out between 600 apartment and 800 non-apartment residents in the Shiraz (Southern Region of Iran) through multi-stage cluster sampling during the year 2011. The statistical analysis was performed on the obtained data using multi-variable logistic regression as well as ANOVA analysis. RESULT: the rate for apartment-living in above 30 years old age group was 8.31 times more than 15-30 years old, 9.6 times more in employed vs. unemployed; 6.57 and 9.49 times more in families with average and high monthly incomes, respectively, vs. family with low monthly income; 8.73 times more in owner sub-group vs. renter sub-group; and 1.30 times more in people living lonely than those living with family. People living in an apartment are in poor health status considering physical, mental and social aspects. CONCLUSION: Based on the results, it is very important that policy makers in urban areas consider the determinative role of socio-economic and demographic factors, which are involved in selecting apartment-living lifestyle by urban residents and also are effective on health.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".