Explaining the process of formation of ageism among the iranian older adults
Bibliographic record
Abstract
BACKGROUND: Ageism is considered as one of the consequences of the industrialization of societies, which appears in various forms in different cultures. This study aimed to explain the process of formation of ageism among the older adults people. METHODS: The research was conducted using grounded theory method. Data were collected from 28 participants using in-depth semi-structured interviews and field notes. Data were analyzed using open, axial, and selective coding. RESULTS: Fear of loneliness and rejection striving to tackle ageism "was identified as the core category of the study. Concepts such as "family context" and "cultural context" were relevant. After identifying the strategies used by the older adults in response to the context ("maintaining integrity", "socio-cultural care" and "proper health care", "striving to tackle ageism") was the most important process in ageism by the Iranian older adults. CONCLUSION: Findings of this study indicated that individual, family and social factors play an important role in the process of ageism among the older adults. These factors sometimes exacerbate or moderate the process of ageism. By recognizing these factors, various social institutions and organizations (including the health care system and the national media (radio and television)) can help the older adults achieve successful aging by emphasizing the issues related to the social aspect.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".