Psychiatric Morbidities, Neurocognitive Impairment, Quality of Life, And Burden of Care in Elderly Patients: A Tertiary Care Hospital-Based Cross-Sectional Study
Bibliographic record
Abstract
Background: The elderly population suffers from disability and functional impairment due to the increasing age and changing social circumstances. Besides physical illnesses, psychiatric morbidities are frequently encountered among elderly individuals. Functional dependency for the activities of daily living is common among elderly people. All these collectively hamper quality of life in elderly patients and increases the burden of caregivers as well. With this background, the present study was carried out to explore the psychiatric morbidities, quality of life and caregiver burden in elderly population. Aim and Objectives: To study sociodemographic profile, psychiatric morbidities, neurocognitive impairment, quality of life, and burden of care in elderly patients. Method: This was a hospital-based cross-sectional observational study conducted at a tertiary care center. 195 consecutive patients from the various outpatient departments fulfilling the study criteria were enrolled. Subjects were interviewed using a pre-designed study pro-forma. The Montreal Cognitive Assessment (MoCA), the 36-items Short Forn Survey (SF 36) and the Zarit Burden Interview (ZBI) were used for cognitive impairment, quality of life and caregiver burden, respectively. Data was analyzed on SPSS 22.0 using appropriate statistical methods. Results: The mean age of patients was 67.59± 6.09 years. Majority of patients were males (63.08%), married (82.6%), uneducated (43.6%) and unemployed (49.23%) and were staying in a Joint family. 35.38% (N=69) patients were found to be having psychiatric morbidity among which most prevalent class of psychiatric morbidity was Depressive disorder (30.43%). One fourth (24.62%) of the patients had cognitive impairment on MoCA assessment. Mean scores ranging from 30 to 60 out of 100 in Health-Related Quality of life were obtained in all domains of SF-36 with lowest in Physical role limitation domain. The mean score of caregiver burden (ZBI) was 26.12. More than half 59.49% of the caregiver reported as feeling burdened ranging from mild to severe burden, while 40.51% of the caregivers reported no burden at all. The caregivers who were burdened, 72.5% had patients with psychiatric illness, while 52.4% were burdened with patients other than psychiatric morbidity. Conclusion: Despite adequate healthcare, the main issues concerning aging and mental health are prevention, early recognition of major psychiatric morbidities, treatment and quality of life interventions. There is a need to undertake routine screening of psychiatric disorders in geriatric population considering the common occurrence in them. Early diagnosis and timely intervention can improve the quality of life in the elderly and reduce the burden of care in their caregivers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".