A Comparative Study of the Coverage of the Concept of Successful Aging in the Health Programs of Iran and Selected Countries
Bibliographic record
Abstract
Background. Successful aging is the feeling of inner satisfaction and happiness of the person from his/her current and past life. Implementation of this concept in societies requires attention in macro plans and policies. This study, therefore, aimed to study the coverage of the concept of successful aging in the health programs of Iran and selected countries. Methods. In this comparative study, the literature was first reviewed. To this end, the internal websites were searched and the documents and circulars of the Ministry of Health along with the developed and implemented programs regarding the elderly were extracted. Then, the components highlighted in each program were identified. Finally, the elements and components of the coverage of the concept of successful aging in the health programs of Iran was investigated with other selected countries in this field and the existing gaps were compared. Results. Elements and components of the successful aging in all programs were determined after examining the country's health programs. The sub-components of health promotion, provision of appropriate health services, as well as medical and treatment services were highlighted in most of the country's health program packages; however, the sub-components of disease prevention, long-term care, adherence of the elderly to the application of provided services, social support, education and literacy, income, insurance and social support, as well as employment were not taken into consideration in any of the programs. The health programs of the two countries (i.e., Canada and the United States) out of all other examined countries were highly compatible with the components of successful aging in the framework developed by the World Health Organization. Conclusion. Since no importance was attached to the components such as prevention of diseases, long-term care, and adherence of the elderly to the application of provided services, it was recommended that the health policy makers should fill the existing gap by following the examples of successful old age programs in the selected countries. Moreover, it was found absolutely necessary to provide components such as social support, education and literacy, insurance and employment, as well as inter-sectoral conflict at the welfare, education, economy, and finance ministerial levels.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".