Mapping an evidence-based end-of-life care framework for older adults in Chinese nursing homes: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: End-of-life care is essential for older adults aged ≥60, particularly those residing in long-term care facilities, such as nursing homes, which are known for their home-like environments compared with hospitals. Due to potential limitations in medical resources, collaboration with external healthcare providers is crucial to ensure comprehensive services within these settings. Previous studies have primarily focused on team-based models for end-of-life care in hospitals and home-based settings. However, there is a lack of sufficient evidence on practices in such facilities, particularly for Chinese older adults. The aim of this scoping review is to map the existing literature and inform the development of an appropriate care framework for end-of-life care in nursing homes. The focus of this article will be on the scope of services, guidelines for decision making, roles within interdisciplinary teams, and the practical feasibility of care provision. METHODS AND ANALYSIS: A systematic search will be conducted across nine electronic databases: PubMed, Scopus, EMBASE, Cochrane, PsycINFO, ERIC, CINAHL, China National Knowledge Infrastructure (CNKI), and Wanfang Data. The search will identify literature published in English and Chinese from January 2012 onwards. Articles will be selected based on their relevance to older adults aged ≥60 with disabilities or life-threatening chronic conditions receiving end-of-life care in nursing homes or similar settings. The data extraction process will be guided by the Canadian Hospice Palliative Care Association model (CHPCA) and the Respectful Death model. Qualitative data analysis will be performed using a framework method and thematic analysis, employing both inductive and deductive approaches, with three reviewers participating in the review process. ETHICS AND DISSEMINATION: Ethical approval is not required because the data for this review is obtained from selected publicly available articles. The results will be disseminated through publications in peer-reviewed journals and presented at relevant conferences. Furthermore, the findings will be shared with policymakers and healthcare professionals engaged in end-of-life care to inform practice and decision making. STUDY REGISTRATION: The review protocol has been registered on osf.io (https://osf.io/3u4mp).
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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 | 0.095 | 0.089 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".