Prevalence and Associated Factors of Depression among Elderly Population Living in Geriatric Homes in Kathmandu Valley
Bibliographic record
Abstract
Introduction: Depression generally presents in all age group but is more common among elderly population living in geriatric homes. Despite the growth of geriatric home health services, little is known about the mental health needs of geriatric people seen in their homes. Methods: This study is conducted in July – December 2010 to determine the prevalence and associated factors of depression among elderly population living in geriatric homes in Kathmandu Valley. A total of 78 elderly people were included in this study. Semi-structured questionnaires and in-depth interview guidelines were used to further exploring the associated factors of depression. The study participants were identified by using Geriatric Depression Scale. Results: The prevalence of depression was 51.3% with severe depression 15.4% and mild depression 35.9%. Most of the severely depressed respondents (75%) were widow/widower; most of the mild depressed respondents (85.7%) were illiterate; three quarter (75%) of severely depressed respondents had no children; and almost all of severely depressed respondents (90.9%) had difficulty in daily living activities due to health problems Conclusion: Majority of respondents were found to be living with varying level of depression. Poverty, presence of physical illness and lacking social and family support contributes to depression. Majority of depressed respondents mentioned their satisfaction with the living in geriatric home. DOI: http://dx.doi.org/10.2126/joim.v35i1.8896 Journal of Institute of Medicine, April, 2013; 35:39-44
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".