Development and Pilot Evaluation of a Novel Dignity-Conserving End-of-Life (EoL) Care Model for Nursing Homes in Chinese Societies
Bibliographic record
Abstract
PURPOSE OF THE STUDY: The provision of end-of-life (EoL) care in long-term-care settings remains largely underdeveloped in most Chinese societies, and nursing home residents often fail to obtain good care as they approach death. This paper systematically describes the development and implementation mechanisms of a novel Dignity-Conserving EoL Care model that has been successfully adopted by three nursing homes in Hong Kong and presents preliminary evidence of its effectiveness on enhancing dignity and quality of life (QoL) of terminally ill residents. DESIGN AND METHODS: Nine terminally ill nursing home residents completed the McGill Quality of Life Questionnaire and the Nursing Facilities Quality of Life Questionnaire at baseline and 6 months post-EoL program enrollment. Wilcoxon signed rank test was used to detect significance changes in each QoL domains across time. RESULTS: Although significant deterioration was recorded for physical QoL, significant improvement was observed for social QoL. Moreover, a clear trend toward significant improvements was identified for the QoL domains of individuality and relationships. IMPLICATIONS: A holistic and compassionate caring environment, together with the core principles of family-centered care, interagency and interdisciplinary teamwork, as well as cultural-specific psycho-socio-spiritual support, are all essential elements for optimizing QoL and promoting death with dignity for nursing home residents facing morality. This study provides a useful framework to facilitate the future development of EoL care in long-term-care settings in the Chinese context.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".