Identifying Core Domains to Assess the “Quality of Death”: A Scoping Review
Bibliographic record
Abstract
CONTEXT: There is growing recognition of the value to patients, families, society, and health systems in providing healthcare, including end-of-life care, that is consistent with both patient preferences and clinical guidelines. OBJECTIVES: Identify the core domains and subdomains that can be used to evaluate the performance of end-of-life care within and across health systems. METHODS: PubMed/MEDLINE (NCBI), PsycINFO (ProQuest), and CINAHL (EBSCO) databases were searched for peer-reviewed journal articles published prior to February 22, 2020. The SPIDER tool was used to determine search terms. A priori criteria were followed with independent review to identify relevant articles. RESULTS: A total of 309 eligible articles were identified out of 2728 discrete results. The articles represent perspectives from the broader health system (11), patients (70), family and informal caregivers (65), healthcare professionals (43), multiple viewpoints (110), and others (10). The most common condition of focus was cancer (103) and the majority (245) of the studies concentrated on high-income country contexts. The review identified five domains and 11 subdomains focused on structural factors relevant to end-of-life care at the broader health system level, and two domains and 22 subdomains focused on experiential aspects of end-of-life care from the patient and family perspectives. The structural health system domains were: 1) stewardship and governance, 2) resource generation, 3) financing and financial protection, 4) service provision, and 5) access to care. The experiential domains were: 1) quality of care, and 2) quality of communication. CONCLUSION: The review affirms the need for a people-centered approach to managing the delicate process and period of accepting and preparing for the end of life. The identified structural and experiential factors pertinent to the "quality of death" will prove invaluable for future efforts aimed to quantify health system performance in the end-of-life period.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".