From Needs and Dilemmas Facing View of Elderly People Living with HIV/AIDS Long-Term Care Measures in Taiwan
Bibliographic record
Abstract
The life of people living with HIV has been prolonged with HAART, and since 1997 the introduction of antiretroviral HAART in Taiwan has increased the survival rate of infected people to 85.9%. Therefore, with the extension of the life of people living with HIV and the entry into the old age, how to provide suitable long-term care services is an issue that Taiwan policy needs to face and think. This research through surveys and interviews to find Taiwan elderly people living with HIV in Taiwan needs and plight of the contains (1) diseases and health care issues, (2) social prejudice and discrimination (3) psychology and adjustment of the identity and reflection (4) adjustment of interpersonal relationships. According to the empirical data shows Taiwan's long-term care measures in difficulties arising in the care for older people living with HIV (1) non-suitable for elderly people living with HI community long-term care services; (2) long-term care institution the exclusion of people living with HIV (3) lack of financial resources of older living with HIV with using institutional long term care. (4) the incoherence of HIV medical and long-term care measures. (5) course focuses on long-term care health care, neglect the psychosocial dimensions of older people living with HIV. This study attempts to present long-term care of the elderly people living with HIV needs and challenges and dilemmas facing in Taiwan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".