Coming together as a whole: lessons from connecting across sectors, cultures, and contexts to elevate efforts for planetary health
Bibliographic record
Abstract
BACKGROUND: Addressing complex and interconnected ecological, social, and health issues necessitates upstream, solutions-oriented, and whole-systems thinking. Specifically, exploring what it means to live in reciprocity with the planet and all living systems, now and for generations to come, can have a crucial role in advancing planetary health. METHODS: In this presentation, we show findings from four gatherings that we co-designed and co-hosted to connect communities, lands, waters, climate, and health. We draw lessons from two events in the Cowichan watershed (co-hosted by Cowichan Tribes) and the Stellako and Nechako watersheds (co-hosted by Stellat'en First Nation) that were co-designed with the ECHO Network. These gatherings brought together youth, Indigenous leaders, researchers, and health, community, and environmental decision-makers across British Columbia to learn how to address connected health, environmental, and community concerns. We supplement these findings with insights from two co-hosted conversations, that connected five continents and more than 40 countries, each enabling 24 h of continuous dialogue on the theme of working together for a healthy, just, and sustainable planet. FINDINGS: Gatherings took place between Oct 12, 2022, and Dec 1, 2023. These gatherings each provided lessons about how Indigenous-led, integrative approaches that connect the health of people to lands, waters, and ecosystems can elevate and enhance our work and scholarship to address ecological, social, and health issues. Specifically, initiatives co-designed in this way help overcome challenges that arise when addressing boundary-crossing, intersectional and intersectoral issues at the nexus of climate, public health, and planetary health. Our findings include insights into strengthening research and public health capacity for integrative approaches to complex issues that are relevant to place-based contexts and have implications and applications for planetary health. INTERPRETATION: Our presentation summarises how these gatherings-each of which were fuelled by a sense of love for the planet, each other, and future generations-have progressed intersectoral, intergenerational, boundary-crossing, and transformative approaches to planetary health. These insights help guide future directions for planetary health research, practice, and policy. FUNDING: The Canadian Institute of Health Research Environment (IP4150712), Michael Smith Health Research BC (RA-2022-2872), and Vancouver Foundation (FOI19-1781).
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.041 | 0.044 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.006 | 0.010 |
| 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".