AB001. Community capacity development to enhance hospice palliative care in Alberta, Canada communities: evidence demonstrating the value of a community engaged model
Bibliographic record
Abstract
Our study explored the value of a community engaged model for good hospice care in three rural communities in Alberta, Canada. When communities are highly engaged in planning and implementing hospice care in their communities, our study discovered that they have key characteristics: that volunteerism needs to be balanced to prevent burnout; that the local knowledge of community members is used in a number of ways to plan and provide good hospice care; that a variety of resources, infrastructure, policies and expertise are used by the community to nurture community-focused palliative care initiatives. The value to the community or social capital, that accrues from these initiatives is not easily appreciated by the community members, and community-based initiatives benefit when this value is identified for them. In all three communities a focus group was conducted separately with the Hospice Society board and with family members and volunteers connected with the Hospice Society. Participants attending this oral presentation will learn how community palliative care is perceived by non-professional community leaders, as well as strategies that may help address barriers that are encountered when communities become engaged in addressing their own hospice and end of life care needs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".