Integrating Older Adults’ Voices on Climate change Into One Health Framework to Promote Healthy Aging
Bibliographic record
Abstract
Abstract Climate change poses threats to population health, affecting not only physical health, but also mental, social, and environmental well-being. Older adults are especially vulnerable, yet their perspectives are often overlooked in climate and health research. This qualitative pilot study foregrounds the voices of older adults, applying the One Health Framework to examine their perspectives on how climate change affects overall health. Employing an autophotography process and semi-structured interviews, the study also explores older adults’ knowledge and beliefs about climate-related issues. Seventeen community-based older adults were recruited and 14 of them completed the study. Participants are white, aged 67-85, and mostly with graduate degrees. Constant comparison was used to analyze data. Key themes included 1) Vulnerability—participants highlighted challenges such as self-care and inadequate infrastructure during climate change and linked these challenges to their impacts on participants’ care for plants and wildlife; 2) Recognizing Ecosystem Disruption—participants noted disruption of local weather patterns, and food systems and changing patterns in plant and wildlife interactions; and 3) Nurturing Resilience—participants described adapting strategies, including engaging in energy saving, plant care and sustaining strong bonds with wildlife as sources of well-being. Participants emphasized the need for cross-sector collaboration and intergenerational knowledge sharing to strengthen resilience. Findings indicate that acknowledging older adults’ perspectives is critical for achieving holistic health goals. Within the One Health Framework, these insights highlight the interconnectedness of human, wildlife, and environmental health, and confirm that older adults’ health outcomes cannot be addressed in isolation from ecological and social systems.
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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 | 0.020 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".