Low Hospice Utilization in New York State: Framework for Compiling and Ranking Barriers
Bibliographic record
Abstract
Background: The hospice benefit can improve end-of-life outcomes, but is underutilized, particularly in low enrollment states such as New York. Little is known about this underutilization. Objective: The first part of a mixed-methods study aimed to compile and rank barriers to hospice utilization and identify differences between New York and the rest of the United States. Setting/Subjects and Design: Clinicians, administrators, and hospice employees participated in six sessions (6–12 per session) across New York State, USA. During each session, a methodology known as nominal group technique was used to elicit barriers to hospice, identify those specific to New York, and suggest interventions to improve access. The analysis involved first categorizing and ranking barriers, and then conducting a thematic analysis of session transcripts to examine barriers specific to New York and proposed interventions to improve utilization. Results: Fifty-seven participants ranked 54 barriers, which were grouped into nine categories. These reflected concerns about clinician knowledge and attitudes or beliefs; patient and family knowledge, attitudes or beliefs, and resources; and both structural elements and practices of hospices, nursing homes, palliative care services, and other entities in the health care system. Thirteen barriers from eight categories were ranked among the top five by ≥10% of participants; only 10 of the 54 were judged to be specific to New York. Thematic analysis highlighted 14 barriers important in New York and suggested 11 interventions to improve hospice access. Conclusions: A categorization and ranking of barriers may guide future interventions to improve low hospice utilization. Novel studies with heterogeneous stakeholders are needed.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.011 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".