An Analysis of Journey Mapping to Create a Palliative Care Pathway in a Canadian First Nations Community: Implications for Service Integration and Policy Development
Bibliographic record
Abstract
Providing palliative care in Indigenous communities is of growing international interest. This study describes and analyzes a unique journey mapping process undertaken in a First Nations community in rural Canada. The goal of this participatory action research was to improve quality and access to palliative care at home by better integrating First Nations' health services and urban non-Indigenous health services. Four journey mapping workshops were conducted to create a care pathway which was implemented with 6 clients. Workshop data were analyzed for learnings and promising practices. A follow-up focus group, workshop, and health care provider surveys identified the perceived benefits as improved service integration, improved palliative care, relationship building, communication, and partnerships. It is concluded that journey mapping improves service integration and is a promising practice for other First Nations communities. The implications for creating new policy to support developing culturally appropriate palliative care programs and cross-jurisdictional integration between the federal and provincial health services are discussed. Future research is required using an Indigenous paradigm.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".