Common Components of Efficacious In‐Home End‐of‐Life Care Programs: A Review of Systematic Reviews
Bibliographic record
Abstract
Multiple randomized controlled trials on in-home end-of-life (EOL) programs, often with multifaceted and varying components, have shown benefits and reduced costs. The objective of this review was to determine which components of these programs are most commonly associated with better outcomes than usual care. MEDLINE, CINAHL, and the Cochrane Library databases were searched from 2003 to 2014 for reviews of studies of in-home programs treating individuals with advanced illness. Original quantitative studies were included from these reviews, and the details of every program that had a significant positive effect on any outcome measured were extracted. Nineteen reviews met the inclusion criteria, from which 40 relevant studies were identified. Thirty unique components emerged from the content analysis of the program descriptions. On average, each program contained 11 components; the six most common were linkage with acute care, multidisciplinary nature, EOL expertise and training, holistic care, pain and symptom management, and professional psychosocial support. Linkage, around-the-clock availability, and customized care planning were most common to the nine interventions for which a significant cost reduction was reported. Efficacious in-home EOL programs comprised multiple components. Knowledge of these features can help inform the design of this care in local contexts that will more likely improve outcomes for individuals in an effective and cost-efficient manner.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".