How Chronic Pain Impacts the Quality of Life in Older Adults Living in Long-term Care
Bibliographic record
Abstract
Chronic pain profoundly affects the quality of life for older adults in long-term care facilities. This study explores its multifaceted impact on well-being and examines mitigation strategies through empirical, aesthetic, and ethical perspectives. Focusing on research, clinical expertise, and experience, it investigates how chronic pain influences sleep, mental health, and nutrition. Chronic pain disrupts sleep patterns, leading to fatigue, diminished cognitive function, and other chronic health issues, which collectively reduce quality of life. It also affects mental health, causing psychological distress that further diminishes life quality for older adults in long-term care. Nutrition is another area adversely affected by chronic pain. Older adults with chronic pain often struggle to maintain a balanced diet, leading to either insufficient nutrient intake and weight loss or overconsumption and weight gain. Both malnutrition and over-nutrition can exacerbate health problems, further reducing quality of life. To address these challenges, the paper proposes strategies to prevent or reduce the decline in quality of life caused by chronic pain. This includes implementing preventive measures, offering support to older adults, and focusing on new research or methods specifically targeting those already suffering from chronic pain to alleviate their discomfort. This paper highlights the significant impact of chronic pain on older adults in long-term care facilities and calls for healthcare workers to address these issues. By understanding how chronic pain affects various aspects of life, there is a push for more research and strategies to alleviate pain-related suffering and enhance the quality of life for these individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".