Prevalence of depression among elderly people in the Al-Nasiriya province
Bibliographic record
Abstract
Search, G -Funds CollectionBackground.Depression is one of the major diseases that can lead to undesirable results, including decreased productivity, more demands on healthcare resources and sometimes even death.Objectives.The purpose of this research was to examine the incidence of depression and risk variables among residents of the Al-Nasiriya province who are 60 year of age and older.Material and method.This community-based cross-sectional survey was carried out between 1 February 2022 and 1 May 2022.The Geriatric Depression Scale (GDS) -the short version (with 15 items) -and a form of socio-demographic variables were used to interview 150 elderly people from the general population and primary healthcare clinics in the Al-Nasiriya province in the south of Iraq.Results.Depression is more prevalent in older people than in younger people.56.6% of the population is considered not to be depressed, 43.33% are depressed, 22.33% are mildly depressed, 11.33% are moderately depressed, and 9% are severely depressed.This research shows that being a woman, being between the ages of 60 and 69, being retired, being married, being illiterate, having owned a home, suffering from a chronic illness and taking multiple medications daily, not having a physical disability or a family history of mental illness, not smoking and having a large family are all significant risk factors for depression.There is a substantial correlation when the p-value is less than 0.05. Conclusions.The results of this study show that more than a third of elderly people have mild depression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".