Supporting the Journey Home: The Process of Co-designing an Education Program to Strengthen Palliative Care Capacity in First Nations Communities
Bibliographic record
Abstract
In Ontario, Canada, several training programs have been created to improve home-based palliative care in First Nations communities, though they primarily focus on meeting needs at end-of-life. Therefore, education focused on incorporating an early palliative care approach for community health-related workers is necessary. To address this gap, we tested the CAPACITI curriculum with 12 health care providers working in First Nations communities across Ontario, 11 of which were members of a First Nations community, and engaged them in a collaborative process to co-design an education program that they felt was representative of First Nations values and culture. The co-designers were trained as nurses (n=8), personal support workers (n=2), a social worker (n=1), and a physician (n=1). We met with them for 12 weekly one hour sessions. They completed a workbook of questions and recommendations to tailor the education to a First Nations community context. We incorporated these recommendations into the new education by reviewing existing material, making notes of suggested changes, and adding new content. We redesigned the education according to several themes: incorporating culture, recognizing First Nations health care workers and knowledge, and approaching education wholistically. The resulting program, Supporting the Journey Home: Growing the Community Bundle to Care for those with Serious Illness, gives First Nations health care providers practical resources to operationalize an early palliative care approach with community members. To our knowledge, this is the first study to describe the co-design process of an existing palliative care education program with First Nations health care providers.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".