Connecting communities across the globe: Atlas protocol
Bibliographic record
Abstract
Background: Providing universal access to palliative care is increasingly recognised as a global public health priority, especially in low- and middle-income countries. Compassionate communities could help with provision by fostering community-led responses to dying, death and grief. Despite their global growth, compassionate communities are often absent from national and international palliative care strategies, and few are represented in academic literature. This, along with their diversity and community-defined nature, can contribute to difficulties in documentation, evaluation and visibility, limiting the ability to showcase their benefits, form partnerships and evaluate practice. Objectives: This study protocol outlines the development of the first global atlas of compassionate communities, aiming to map their locations and increase understanding of their structures, activities and impacts. Design: A participatory methodology was used. This enabled global participants to complete and share the survey based on their experiences with community programmes addressing serious illness, dying and grief. A diverse steering committee guided the design, validation and piloting of the survey to ensure clarity, cultural sensitivity and accessibility. Eighteen global experts contributed to developing and validating the survey, with 14 of 15 items meeting the Content Validity Index threshold. The final survey captures data on location, aims, evaluation, challenges and impact. Dissemination involves global networks, social media and snowball sampling. Discussion: This protocol addresses a critical gap in Public Health Palliative Care literature by providing an inclusive and participatory method to map the compassionate community's landscape. The resulting data will promote visibility, partnerships and future research, supporting greater recognition of global compassionate communities and their contributions to primary palliative care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".