<b>P-72</b> Building research capacity to address the public health need for palliative care
Bibliographic record
Abstract
Background The growing demand to address significant public health issues such as ageing populations, changing patterns of chronic diseases, individuals living longer with life-limiting conditions and so on increases the pressure on researchers to produce high quality, clinically relevant, innovative palliative care research so as to improve the quality of life for service users and carers. Aim All Ireland Institute of Hospice and Palliative Care (AIIHPC) believes that one way to meet this demand is through research capacity building which is being taken forward via the Institute’s Palliative Care Research Network. Method Capacity building is defined as a “process where individuals, groups, networks, organisation and the wider community are encouraged and facilitated in enhancing their knowledge and skills so as to increase their ability to perform innovative and high quality research” (Moley and Seale, 2009). It is a major component of large national and international funding that seek to enhance and develop research expertise and leadership in specific health areas, for example, Irish Health Research Board, Public Health Agency (Northern Ireland), Horizon 2020, Canadian Institutes of Health Research). Conclusion AIIHPC’s Palliative Care Research Network is taking forward a multi-tiered approach to research capacity building ranging from awareness-raising of research, to providing more extensive support to those who want to progress elements of palliative care research, to providing tailored support and leadership opportunities for more advanced researchers.
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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.130 | 0.081 |
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".