PALLIATIVE CARE AND END OF LIFE CARE IN LATIN AMERICA: SCOPING REVIEW
Bibliographic record
Abstract
INTRODUCTION: Populational aging and improved treatments for chronic non-communicable diseases extend life expectancy but not always quality of life. By 2060, 48 million people are expected to die of serious illnesses, and 83% of these deaths will occur in developing countries. Only 14% of those who needed palliative care receive it. AIM: To describe the methodological trends, thematic areas, populations studied, and future challenges in Latin American regions with respect to adult palliative care. METHODS: A scoping review of 60 articles from 2010 to 2019 in indexed journals in English, Spanish, and Portuguese was conducted. RESULTS: Most articles were from Brazil, Colombia, and Mexico. Patients, caregivers, healthcare professionals, and students constituted the primary study population. Quality of Life, knowledge, and costs of attention were also assessed. It appears that early palliative care improves the outcomes of patients, caregivers, and health care professionals, however, the disparity in palliative care services between Latin America, US, UK, Canada, and Spain is concerning. CONCLUSIONS: Globally, more palliative care is needed, especially in Latin America. However, there are not enough graduate palliative care programs. Academic palliative care education must be promoted. Communication between the interdisciplinary team, the patient, and the caregiver is critical. While the region's scientific literature output has improved, many knowledge gaps remain. For patients' sake, governments should regulate, create, and facilitate palliative care services.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".