OA21 The power to choose: developing community capacity to provide palliative care in four first nations communities in canada
Bibliographic record
Abstract
BACKGROUND: First Nations people in Canada are ageing with a high burden of chronic, progressive, and life-limiting illness. The majority now die in urban hospitals or long term care homes far away from family and culture. Today, First Nations community leaders are working to build local community capacity to support people and their families who choose to die at home. AIM: This presentation describes a five year participatory action research project that has enhanced local palliative care capacity in four First Nations communities using innovative strategies for collaboration, education, and advocacy (www.eolfn.lakeheadu.ca). METHODS: The process of community capacity development was locally driven and controlled. A local leader and advisory committee implemented a palliative care community assessment that identified resources and needs for community awareness, health provider education, service development and improved partnerships with external health care providers (physicians, hospitals etc) providing care to community members. Innovative strategies to address these needs were developed, implemented and evaluated. RESULTS: If services and community supports were available, 87% of the FN community participants would prefer to die at home. Each of the four participating FN communities developed a unique palliative care program model responsive to community culture and context. Culturally appropriate videos and print resources for education and community development were created to share internationally. CONCLUSION: First Nations communities have the desire and capacity to care for community members to the end of their lives. Community development and advocacy are required to support First Nations in addressing existing barriers and gaps in education, policy and service delivery.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".