The effect of end-of-life decision-making tools on patient and family-related outcomes of care among ethnocultural minorities: A systematic review
Bibliographic record
Abstract
BACKGROUND: End-of-life decision-making tools are used to establish a shared understanding among patients, families and healthcare providers about medical treatment and goals of care. This systematic review aimed to understand the availability and effect of end-of-life decision-making tools on: (i) goals of care and advance care planning; (ii) patient and/or family satisfaction and well-being; and (iii) healthcare utilization among racial/ethnic, cultural, and religious minorities. METHODS: A search was conducted in four electronic databases (inception to June 2021). Articles were screened for eligibility using pre-specified criteria. We focused on adult patients (aged ≥18 years) and included primary research articles that used quantitative, qualitative, and mixed-methods designs. Complementary quality assessment tools were used to generate quality scores for individual studies. Extracted data were synthesized by outcome measure for each type of tool, and an overall description of findings showed the range of effects. RESULTS: Among 14,316 retrieved articles, 37 articles were eligible. We found that advance care planning programs (eleven studies), healthcare provider-led interventions (four studies), and linguistically-tailored decision aids (three studies) increased the proportion of patients documenting advance care plans. Educational tools (three studies) strongly reduced patient preferences for life-prolonging care. Palliative care consultations (three studies) were strongly associated with do-not-resuscitate orders. Advance care planning programs (three studies) significantly influenced the quality of patient-clinician communication and healthcare provider-led interventions (two studies) significantly influenced perceived patient quality of life. CONCLUSION: This review identified several end-of-life decision-making tools with impact on patient and family-related outcomes of care among ethnocultural minorities. Advance care planning programs, healthcare provider-led interventions and decision aids increased documentation of end-of-life care plans and do-not-resuscitate orders, and educational tools reduced preferences for life-prolonging care. Further research is needed to investigate the effect of tools on healthcare utilization, and with specific patient population subgroups across different illness trajectories and healthcare settings.
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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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".