Comparison of The Hospice Palliative Care Delivery Systems in Iran and Selected Countries
Bibliographic record
Abstract
Background: There is an increasing demand for Hospice Palliative Care (HPC) due to the aging population, increased incidence of cancer, and other chronic diseases, as well as recent advances in care and treatment. Objectives: The present study was conducted to examine the nature and structure of HPC services and to describe and compare them in the United Kingdom (UK), Canada, Australia, Japan, India, Jordan, and Iran to extract general conclusions and suggestions for developing HPC systems in Iran. Methods: In the current descriptive-comparative study, from 2018 to 2019, HPC delivery systems in the selected countries and Iran were reviewed based on the World Health Organization (WHO) guideline, and the similarities and differences among them were explained. Results: Developing the National HPC Program and its integration into the health system are important activities. The most common source of financing is donation. The services are mainly provided to patients with cancer. Human resource development includes curriculum reform, creating specialty, subspecialty disciplines, and holding training courses. Other activities include designing national guidelines, the free access to opioids, research development, the establishment of the national information network, and the quality control programs. Iran lacks any formal structure and program of HPC services and they are provided in a scattered and very limited manner as part of general palliative services. Conclusions: HPC services are in a mediate and low level in developed countries and Iran, respectively. Before the establishment of the HPC delivery system, a complicated range of economic, social, cultural, and political factors must be considered.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".