The need and satisfaction of the patients of hospice services in Varna Region
Bibliographic record
Abstract
Introduction: In the context of the severe demographic situation in our country, the problem of the hospice movement stands out. In an ageing society, people who need hospice care are constantly increasing. Chronically sick patients, especially those with progressive disease, pose a serious problem because they need specific care. Some such care is provided in hospices, which in our country prove to be inadequate. Purpose: To investigate the need and satisfaction of patients from hospice services in Varna region. Material and Methods: The survey was conducted in the first quarter of 2017 and covers 85 people, the users of hospice services offered in some of the hospices in Varna. Sociological methods were used: document analysis, survey method and statistical methods. Results and Discussion: Respondents are of different educational attainment and social status over a broader age range. There are residents of Varna, the surrounding settlements and other big cities in the country. Some of the respondents were staying in different hospices and considered that they were insufficient for the city of Varna. Almost everyone knows the administrative organization and the internal order of the hospice, they are happy with the medical service but have some recommendations for improving the lifestyle. Patients of hospice services are responsible for searching and choosing a hospice service to enter by using different sources of information.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".