Depression in the Iranian Elderly: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Depression can lead to increased medical costs, impaired individual and social functioning, nonadherence to therapeutic proceeding, and even suicide and ultimately affect quality of life. It is important to know the extent of its prevalence for successful planning in this regard. This study was conducted to determine the prevalence of depression in the Iranian elderly. This systematic review and meta-analysis study was done through Medline via PubMed, SCOPUS, Web of Science, ProQuest, SID, Embase, and Magiran with determined keywords. Screening was done on the basis of relevance to the purpose of the study, titles, abstracts, full text, and inclusion and exclusion criteria. The quality of the articles was assessed using the Newcastle-Ottawa standard scale. After primary and secondary screening, 30 articles were finally included in the study. According to the 30 articles reviewed, the prevalence of depression in the Iranian elderly was 52 percent based on the random-effects model (CI 95%: 46-58). According to the results of the present study, depression in the Iranian elderly was moderate to high. Therefore, more exact assessment in terms of depression screening in elderly people seems necessary. Coherent and systematic programs, including psychosocial empowerment counselling for the elderly and workshops for their families, are also needed. Researchers can also use the results of this study for future research.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".