Barriers and facilitators to care for the terminally ill: A cross-country case comparison study of Canada, England, Germany, and the United States
Bibliographic record
Abstract
BACKGROUND: Why do many patients not die at their preferred location? AIM: Analyze system-level characteristics influencing the ability to implement best practices in delivering care for terminally ill adults (barriers and facilitators). DESIGN: Cross-country comparison study from a "most similar-most different" perspective, triangulating evidence from a scoping review of the literature, document analyses, and semi-structured key informant interviews. SETTING: Case study of Canada, England, Germany, and the United States. RESULTS: While similar with regard to leading causes of death, patient needs, and potential avenues to care, different models of service provision were employed in the four countries studied. Although hospice and palliative care services were generally offered with standard care along the disease continuum and in various settings, and featured common elements such as physical, psycho-social, and spiritual care, outcomes (access, utilization, etc.) varied across jurisdictions. Barriers to best practice service provision included legislative (including jurisdictional), regulatory (e.g. education and training), and financial issues as well as public knowledge and perception ("giving up hope") challenges. Advance care planning, dedicated and stable funding toward hospice and palliative care, including caregiver benefits, population aging, and standards of practice and guidelines to hospice and palliative care, were identified as facilitators. CONCLUSION: Successful implementation of effective and efficient best practice approaches to care for the terminally ill, such as shared care, requires concerted action to align these system-level characteristics; many factors were identified as being essential but not sufficient. Policy implementation needs to be tailored to the respective health-care system(s), monitored, and fine-tuned.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".