How does community engagement evolve in different compassionate community contexts? A longitudinal comparative ethnographic research protocol
Bibliographic record
Abstract
Background: Compassionate communities build on health promoting palliative care that aims to address gaps in access, quality, and continuity of care in the context of dying, death, loss, and grief. While community engagement is a core principle of public health palliative care, it has received little attention in empirical studies of compassionate communities. Objectives: The objectives of this research are to describe the process of community engagement initiated by two compassionate communities projects, to understand the influence of contextual factors on community engagement over time, and assess the contribution of community engagement on proximal outcomes and the potential for sustaining compassionate communities. Research Approach and Design: We use a community-based participatory action-research approach to study two compassionate communities initiatives in Montreal (Canada). We develop a longitudinal comparative ethnographic design to study how community engagement evolves in different compassionate communities contexts. Methods and Analysis: Data collection includes focus groups, review of key documents and project logbooks, participant observation, semi-structured interviews with key informants, and questionnaires with a focus on community engagement. Grounded in the ecology of engagement theory and the Canadian compassionate communities evaluation framework, data analysis is structured around longitudinal and comparative axes to assess the evolution of community engagement over time and to explore the contextual factors influencing the process of community engagement and its impacts according to local context. Ethic: This research is approved by the research ethics board of the Centre hospitalier de l'Université de Montréal (approval certificate #18.353). Discussion: Understanding the process of community engagement in two compassionate communities will contribute to a deeper understanding of the relationships between local context, community engagement processes, and their effect on compassionate communities outcomes.
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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 | 0.135 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".