“If you understand you cope better with it”: the role of education in building palliative care capacity in four First Nations communities in Canada
Bibliographic record
Abstract
Abstract Background In Canada, there is a growing need to develop community-based, culturally appropriate palliative care for Indigenous people living in First Nations communities. The public health approach to palliative care, which emphasizes community-based initiatives, is especially relevant in First Nations communities because care is grounded in their distinct social and cultural context. Central to the public health approach are educational strategies that strengthen communities’ capacity to care for their vulnerable members as they die. This paper presents community-based research conducted with First Nations communities in Canada that aimed to assess and address local palliative care educational needs to improve community capacity in palliative care. Methods Participatory action research (PAR) was conducted with four First Nations communities in Canada over a six-year period (2010–2016). The research occurred in three phases. Phase 1: focus groups, interviews and surveys were employed to assess community specific needs and resources. Phase 2: recommendations were developed to guide the PAR process. Phase 3: educational resources were created to address the identified educational needs. These resources were implemented incrementally over 4 years. Ongoing process evaluation was employed, and revisions were made as required. Results Educational needs were identified for patients, families, community members and internal and external health care providers. A wide and comprehensive range of educational resources were created to address those needs. Those culturally appropriate educational resources are available in a very accessible and useable workbook format and are available for use by other Indigenous people and communities. Conclusions This research provides an example of the public health approach and offers implementation strategies around palliative care education. This paper contributes to the international literature on the public health approach to palliative care by presenting a case study from Canada that includes: conducting a culturally appropriate assessment of educational needs, creating recommendations, facilitating development and implementation of educational resources in the community to improve community capacity in palliative care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".