The Iranian population is graying: are we ready?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Iran has gone through sharp demographic changes in the past three decades. Presently, in Iran, there is a lack of health promotional activities targeting the elderly which can lead to a decrease in their quality of life and an increase in their disability rates. Those most vulnerable amongst the elderly are females, who have low education and low socioeconomic status. For them and others, few social services, accessible housing options and long-term care facilities exist. METHODS: Data was gathered using population projections over an 80-year period (1975 - 2055), facilitated by spectrum software prepared by the USAID/Health Policy Initiative with data source derived from projections of the United Nations, World Population Prospects. Projections derived were on the expected population, the median age of the population, population pyramids, total fertility rates, life expectancy, and dependency ratio. RESULTS: Projections showed that by the middle of this century approximately one fifth of the population will be over 60, with the median age of the population almost doubling from what it is today and the dependency ratio increasing steadily. Currently, the resources are not sufficient to address the special needs of an elderly population and are at risk for becoming even more strained over the 80 year span. CONCLUSION: Iran must begin to prepare itself for the impact that a massive ageing population will have in the ensuing years. Recommendations suggest developing policies supportive of accessible and affordable housing and care facilities, establishing community health programs that aid the elderly in continuing to live at home, and strengthening the availability of pension plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it