How compassionate communities are implemented and evaluated in practice: a scoping review
Bibliographic record
Abstract
BACKGROUND: Compassionate communities are rooted in a health promotion approach to palliative care, aiming to support solidarity among community members at the end of life. Hundreds of compassionate communities have been developed internationally in recent years. However, it remains unknown how their implementation on the ground aligns with core strategies of health promotion. The aim of this review is to describe the practical implementation and evaluation of compassionate communities. METHODS: We undertook a scoping review of the empirical peer-reviewed literature on compassionate communities. Bibliographic searches in five databases were developed with information specialists. We included studies in English describing health promotion activities applied to end-of-life and palliative care. Qualitative analysis used inductive and deductive strategies based on existing frameworks for categorization of health promotion activities, barriers and facilitators for implementation and evaluation measures. A participatory research approach with community partners was used to design the review and interpret its findings. RESULTS: Sixty-three articles were included for analysis. 74.6% were published after 2011. Health services organizations and providers are most often engaged as compassionate community leaders, with community members mainly engaged as target users. Adaptation to local culture and social context is the most frequently reported barrier for implementation, with support and external factors mostly reported as facilitators. Early stages of compassionate community development are rarely reported in the literature (stakeholder mobilization, needs assessment, priority-setting). Health promotion strategies tend to focus on the development of personal skills, mainly through the use of education and awareness programs. Few activities focused on strengthening community action and building healthy public policies. Evaluation was reported in 30% of articles, 88% of evaluation being analyzed at the individual level, as opposed to community processes and outcomes. CONCLUSIONS: The empirical literature on compassionate communities demonstrates a wide variety of health promotion practices. Much international experience has been developed in education and awareness programs on death and dying. Health promotion strategies based on community strengthening and policies need to be consolidated. Future research should pay attention to community-led initiatives and evaluations that may not be currently reported in the peer-review literature.
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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 | 0.069 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".