Global warming in the palliative care research environment – adapting to change
Bibliographic record
Abstract
Advocates of palliative care research have often described the cold and difficult environment that has constrained the development of research internationally. The development of palliative care research has been slow over the last few decades and has met with resistance and sometimes hostility to the idea of conducting research in 'vulnerable populations'. The seeds of advocacy for research can be found in palliative care literature from the 1980s and early 1990s. Although we have much to do, we need to recognize that palliative care research development has come a long way. Of particular note is the development of well-funded collaboratives that now exist in Europe, Canada, Australia and the USA. The European Association for Palliative Care and the International Association for Hospice and Palliative Care has recognized the need to develop and promote global research initiatives, with a special focus on developing countries. Time is needed to develop good research evidence and in a more complex healthcare environment takes increasingly more resources to be productive. The increased support (global warming) evident in the increased funding opportunities available to palliative care researchers in a number of countries brings both benefits and challenges. There is evidence that the advocacy of individuals such as Kathleen Foley, Neil MacDonald, Balfour Mount, Vittorio Ventafridda, Robert Twycross and Geoff Hanks is now providing fertile ground and a much friendlier environment for a new generation of interdisciplinary palliative care research. We have achieved many of the goals necessary to avoid failure of the 'palliative care experiment', and need to accept the challenge of our present climate and adapt and take advantage of the change.
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 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 | 0.138 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.021 | 0.034 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".