Gaps in Hospice and Palliative Care Research: A Scoping Review of the North American Literature
Bibliographic record
Abstract
BACKGROUND: The demand for hospice and palliative care is growing as a result of the increase of an aging population, which is most prominent in North America. Despite the importance of the topic and an increase in hospice and palliative care utilization, there still are gaps in research and evidence within the field. AIM: To determine what gaps currently exist in hospice and palliative/end-of-life care research within the context of a North American setting to ensure that future directions are grounded in appropriate evidence. METHODS: Using Arksey and O'Malley's scoping review framework, six peer-reviewed, and four grey electronic literature databases in healthcare and the social sciences were searched in mid-2019. 111 full-text articles were retrieved, with 25 articles and reports meeting the inclusion criteria. Major themes were identified through thematic context analysis: (1) clinical, (2) system access to care, (3) research methodology, and (4) caregiving-related research gaps. RESULTS: Findings include strategies for engaging stakeholder organizations and funding agencies, implications for other stakeholder groups such as clinicians and researchers, and highlight implications for policy (e.g., national framework discussion) and practice (e.g., healthcare provider education and training and public awareness). CONCLUSION: Reviewing and addressing targeted research gaps is essential to inform future directions in Canada and beyond.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.035 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".