Assess Quality of Life Among Iranian Married Women Residing In Rural Places
Bibliographic record
Abstract
It is important how women describe their quality of life. The aim of this study was to evaluate the effects of rural residence on quality of life of the married women. The Wellness and Quality of Life Questionnaire (WHOQOL) was used to assess QOL rural residence in Iranian married women. A total of 1,140 (577 urban and 563 rural) women aged 20-45 years were selected using standard cluster sampling technique in Babol, Iran. The questionnaire with 55 items consists of five domains: physical state, mental/emotional state, stress evaluation, life enjoyment, and overall quality of life. Lower scores in three domains: physical state, mental/emotional state, and stress evaluation mean better QOL. Higher scores in life enjoyment and overall quality of life mean better QOL. Rural residences smoke more and have a lower level of education, higher level physical activity, higher level of good self reported dietary, and lower long term health problems than urban residents. After adjusting confounding variables, logistic regression indicated living in rural settings statistically decrease the probability of having worse quality of life related to physical health (OR 0.67; CI 0.50-0.91), higher life enjoyment (OR 0.44; CI 0.32-0.61), and better overall QOL (OR 0.44; CI 0.37-0.61). The results have been suggested to be useful in order to anticipate greater health care needs of the rural married women and improve their quality of life by providing more opportunities for rural women.
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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 | 0.002 |
| 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".