Optimizing Research on End-of-Life Care for Seniors: The Collective New Emerging Team on End of Life Care for Seniors University of Ottawa, Institute of Palliative Care
Bibliographic record
Abstract
Background: End-of-life care for seniors is an important and neglected area of research. The University of Ottawa Institute of Palliative Care has expanded its research capacity by developing a Canadian Institutes of Health Research (CIHR) funded new emerging team on end-of-life care for seniors. This initiative brings together an interdisciplinary team of researchers from palliative care and geriatrics to develop a comprehensive program of research. Methods: 1) A variety of investigators from the fields of palliative care and geriatrics and disciplines of epidemiology, medicine, nursing, psychology and social work will collaborate on the development of a research agenda focussed on end-of-life care for seniors. 2) The conceptual model for the research program consists of 4 broad interrelated domains that are congruent with the CIHR themes of health services, clinical issues, population health and psychosocial, cultural, spiritual and ethical issues; this framework will guide the research program and all studies emanating from the program. 3) Research studies will focus on 5 areas of inquiry that are central to end-of-life care for seniors: palliative end-of-life care for rural seniors, care settings, burden, role of volunteers, and delirium. Results: This new team has the potential to obtain peer-reviewed funding, recruit and train a new generation of researchers, and build a network of concerned researchers. Conclusions: The new team should ultimately contribute to an improved quality of care for seniors who are approaching death.
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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 | 0.163 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".