Cultural and Socio-Economic Factors on Changes in Aging among Iranian Women
Bibliographic record
Abstract
The aim of the study is to determine the cultural and socio-economic factors that influence changes in aging among Iranian women. This qualitative study was part of a more extensive study designed according to grounded theory method. A purposeful, snowball and theoretical sampling technique was used. Data collection instruments were interviews and field notes. Duration of interviews differed and ranged from 38 to 110 minutes. Data collection process, coding and analysis were performed simultaneously. Collected data were analyzed using the recommended method by Corbin and Straus (1998 and 2008). The factors were formed from 6 subcategories: cultural and socio-economic status in the past, urban/rural life, companionship status, beliefs and attitudes, higher responsibilities of women and women's financial capability. This study explained the various aspects of cultural and socio-economic changes in the elderly participants based on their real experiences.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".