AB006. Collaborative model used to develop a resource guide for communities to enhance their palliative and end of life care: the case of Alberta, Canada
Bibliographic record
Abstract
In response to the desire of community organizations in Alberta for information and guidance as they seek to improve palliative and end of life care in their communities, a large number of organizations collaborated to develop a resource guide. In order to achieve this goal, it was first necessary to identify the common information needs of Alberta communities as they pursue their vision for improving local Palliative and End-of-Life Care. A committee comprised of representatives from Alberta Health Services, Alberta Health, Alberta Hospice Palliative Care Association, Hospice Societies, University researchers, Indigenous Health and Palliative Care physicians surveyed numerous community groups and stakeholders about their information needs. As a result of their feedback several themes were identified which formed the basis of the Resource Guide for Community-based Palliative and End-of-Life Care. This oral presentation will leave participants with a good understanding of how multiple stakeholders can work together to strengthen community-led palliative and end-of-life care, at a provincial level, when there is a common goal.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".