Optimizing Hospice Care: A Scoping Review of Staffing Strategies to Enhance Practice
Bibliographic record
Abstract
Background Investigating hospice staffing models is vital for ensuring patient-centered care by aligning workforce capabilities with evolving patient needs. With the opening of the Hospice Residence at Providence Care Hospital, effective staffing can enhance operational efficiency, staff well-being, retention, and elevate the quality of end-of-life care. Objective: The primary objective is to investigate models of hospice care, staffing characteristics across disciplines, staff qualifications, training and operational metrics. Methods A comprehensive search of academic literature was conducted using MEDLINE, CINAHL, and Embase, following the PRISMA-ScR framework. The review focused on hospice care for adults, covering staffing and care models. Studies on general palliative care, pediatric/NICU care, non-full-text studies, and those published before 2013 were excluded. A grey literature search was also performed. Results Out of 1,579 imported studies, 1,082 were screened, 260 full texts reviewed, and 51 met inclusion criteria. Published between 2013 and 2024, the studies were conducted in North America, Europe, and Asia, covering various designs including randomized control trials, observational, qualitative, and mixed-methods. Patient populations included terminally ill individuals with diagnoses such as cancer, COPD, and heart failure. The majority of studies examined inpatient hospices. Facility sizes ranged from 6 to 33 beds (median = 16). Key topics included staffing models, staff-to-patient ratios, training, volunteer roles, care delivery, and patient satisfaction, with common ratios being 5.5:1 to 6.5:1 for physicians and 1.5:1 to 2.7:1 for nurses. Hospice staff included physicians, nurses, aides, social workers, spiritual care providers, and volunteers, with training ranging from specialized education to minimal instruction. Conclusion Understanding staffing strategies is crucial for hospices to meet growing demand and patient complexity. This review emphasizes optimal staff-to-patient ratios, diverse care models, and the vital role of volunteers. Further research is needed to establish best practices for sustainable, high-quality hospice care. Contributors: Patel, V.; Aghakhani Barfeh, K.; McDonald, H.; Kang, J.; Pranajaya, P.; Toon, D.; Wong, M.; Goldie, C.L.
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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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".