Community capacity building in palliative care: An illustrative case study in rural Northwestern Ontario
Bibliographic record
Abstract
Despite the increasing need for palliative care, it is well recognized that people who are dying in rural and remote areas have less access to palliative care services than their urban counterparts. Barriers to accessing palliative care for rural residents include geographic isolation, shortage of human resources, and lack of palliative care education and training for rural health care providers. Community capacity development has become an accepted practice approach to developing rural health services, essentially 'building on what already exists'. This case study research conducted in northwestern Ontario, Canada, examined the application of a four phase community capacity development model as an intervention to develop a palliative care program in a rural community. Data were collected over the three-year period using a participatory action research approach. Findings illustrated the applicability of the model to guide rural palliative care development and provided details of the community change process. Conclusions support the applicability of the model for use as a theory of change to guide and evaluate rural palliative care service development. Keywords: rural health, rural palliative care, palliative care, community capacity development, participatory action research, rural health teamwork, end-of-life 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".