Bibliographic record
Abstract
Whole-person care is the central concept in the philosophy of palliative care, yet the concept is often not apparent in published articles.Nevertheless, most studies give data relevant to the provision of whole-person care.With this thematic issue, we aim to illustrate that not only are there many ways to generate knowledge relevant to whole-person care, but also that whole-person care is best served by using many different methodologies to study many different topics.The six articles presented here help answer this question: How can we best provide whole-person care?In 1994, the National Cancer Institute of Canada (NCIC) created the Sociobehavioural Cancer Research Network.NCIC recognized that cancer cure research had been well funded for years, but socio-behavioural research had not.Socio-behavioural research can create knowledge to lessen the burden of cancer across the continuum of care and create behavioural strategies that focus on care of the whole person, not just on the disease.NCIC funded five teams, including ours, with $60,000 to $88,000 each for seven years.With this funding, our objective was to improve the quality of socio-behavioural palliative care cancer research in order to optimize the quality of life of people with cancer and their families.Given the broad spectrum of palliative care, we focused on creating new collaborations where there was common interest.We worked to improve each others' projects and those of our trainees and to find and share solutions to research challenges.We adopted the Square of Care, included in the Canadian Hospice Palliative Care Association's A Model to Guide Hospice Palliative Care (1), as our conceptual framework.This framework has two interacting dimensions: steps to guide the process of care, and domains/issues faced by palliative care patients and their families.The articles in this thematic issue are relevant to the process of care (dealing with, for example, settings, such as rural or long-term care; or staff education about decision-making aids), to specific issues faced by patients and families (such as pain management and dementia), and to broader concerns of whole-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.018 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".