Engaging healthcare providers and patients on climate action through physical, emotional and social wellness
Bibliographic record
Abstract
According to the World Health Organization, the health challenges from climate change are many and varied including: Malnutrition due to lack of quality food access. Mental health challenges in addition to severe socioeconomic challenges, through the loss of homes, jobs and needed social connections due to extreme events. Acute illness and the risk of water-borne diseases associated with lack of access to clean water. The increased risk of vector-borne diseases with warmer temperatures. Chronic illnesses associated with heat stress and pollution. Death from cardiovascular and respiratory disease, particularly among vulnerable people as temperatures rise to extreme levels. Both healthcare providers and patients must be engaged on climate change and action. While several medical training institutions are exploring opportunities to embed climate change and health education into their curricula, of importance are the holistic strategies to engage patients on climate action. The challenges are complex, and the data is overwhelming. Patients may not fully comprehend the personal implications of climate change and as citizens, may not understand their role in climate action. We suggest through the creation of a sustainable living mindset based on wellness, it is possible for healthcare providers to create a personal and emotional connection to climate action. The results from workshops with older adults are shared in this paper, demonstrating how the link to physical, emotional and social wellness, can encourage behavior change with respect to dietary and consumption practices as well as increased connection to and protection of greenspaces for health and well-being.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".