Developing a compassionate community: a Canadian conceptual model for community capacity development
Bibliographic record
Abstract
The purpose of this article is to share a Canadian model called Developing a Compassionate Community (DCC) in which aging, dying, caregiving, and grieving are everyone’s responsibility. The model provides a research-informed practice guide for people who choose to adopt a community capacity development approach to developing a compassionate community. Based on 30 years of Canadian research by the author in rural, urban, First Nations communities, and long-term care homes, the DCC model offers a practice theory and practical tool. The model incorporates the principles of community capacity development which are as follows: change is incremental and in phases, but nonlinear and dynamic; the change process takes time; development is essentially about developing people; development builds on existing resources (assets); development cannot be imposed from the outside; and development is ongoing (never-ending). Community capacity development starts with citizens who want to make positive changes in their lives and their community. They become empowered by gaining the knowledge, skills, and resources they need. The community mobilizes around finding solutions rather than discussing problems. Passion propels their action and commitment drives the process. The strategy for change is engaging, empowering, and educating community members to act on their own behalf. It requires mobilizing networks of families, friends, and neighbors across the community, wherever people live, work, or play. Community networks are encouraged to prepare for later life, and for giving and getting help among themselves. This Canadian model offers communities one approach to developing a compassionate community and is a resource for implementing a public health approach to end-of-life care in Canada. The model is also available to be evaluated for its applicability beyond Canada and is designed to be adapted to new contexts if desired.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.025 | 0.039 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".