Developing Trusted Voices for Planetary Health: Findings from a Clinicians for Planetary Health (C4PH) Workshop
Bibliographic record
Abstract
Climate change, biodiversity loss, and other environmental changes are rapidly impacting the health of people worldwide, but many clinicians and other health professionals feel unprepared to deal with this burgeoning issue. During the Planetary Health Annual Meeting held in Boston in late 2022, the Clinicians for Planetary Health (C4PH) working group hosted a workshop that highlighted the latest findings of clinicians’ attitudes towards climate change, connections with the related fields of lifestyle medicine and integrative health, lessons learned from implementing “one minute for the planet” in a rural Brazilian clinic, and the benefits of clinicians prescribing time in nature for their patients. This article ends with a few suggestions for healthcare providers to begin implementing planetary health into their professional practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".