Bibliographic record
Abstract
Living in a compassionate community is not a new practice in First Nations communities; they have always recognized dying as a social experience. First Nations hold extensive traditional knowledge and have community-based practices to support the personal, familial, and community experiences surrounding end-of-life. However, western health systems were imposed and typically did not support these social and cultural practices at end of life. In fact, the different expectations of western medicine and the community related to end of life care has created stress and misunderstanding for both. One solution is for First Nations communities to develop palliative care programs so that people can receive care at home amongst their family, community and culture. Our research project "Improving End-of-Life Care in First Nations Communities" (EOLFN) was funded by the Canadian Institutes of Health Research [2010-2015] and was conducted in partnership with four First Nations communities in Canada (see www.eolfn.lakeheadu.ca). Results included a community capacity development approach to support Indigenous models of care at end-of-life. The workshop will describe the community capacity development process used to develop palliative care programs in First Nations communities. It will highlight the foundation to this approach, namely, grounding the program in community values and principles, rooted in individual, family, community and culture. Two First Nations communities will share stories about their experiences developing their own palliative care programs, which celebrated cultural capacity in their communities while enhancing medical palliative care services in a way that respected and integrated with their community cultural practices. This workshop shares the experiences of two First Nations communities who developed palliative care programs by building upon community culture, values and principles. The underlying model guiding development is shared.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.242 | 0.029 |
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".