The Effects of Stigma on the Caregivers of Elderly Patients With Psychiatric Issues in a Chinese Community
Bibliographic record
Abstract
Abstract World Health Organization in 2017 indicates the proportion of persons over the age of 60 years will exponentially grow from 12% in 2015 to 22% in 2050. Advanced age is a common risk factor for multiple conditions, including psychosis, depression, and other mental illnesses linked to cognitive and neurologic disorders. The majority of the studies identify ethnicity and socioeconomic status as primary determinants of mental health care access. Recent studies show that up to 12% of elderly Chinese have had a history of mental problems. However, over 50% of Chinese with mental disorders have failed to obtain professional help. Lack of access to health care for mental disorders has been linked to multiple underlying socioeconomic and cultural factors. These Chinese Americans lack an in-depth understanding of their psychosis, and psychiatric conditions are often a minority in nature. This study will systematically review the existing situations relating the factors to the stigma on caregivers. The result shows that the leading cause of psychiatric disorders, physical and emotional components of the elderly population, needs to be incorporated in the care plan in nursing homes and hospitals. In North America, the constant perception of discrimination and the inherent feeling of isolation and stigma among families with elderly members remains challenging. This review could contribute to the policy reform, which can help design effective control strategies to manage gaps of most mental disorders that continue to disproportionately affect different ethnic groups across the U. S. and Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".