Investigation of climate change impacts on long-term care facility occupants
Bibliographic record
Abstract
Climate change is causing alterations to the geophysical system; rising global temperatures are causing extreme heat events, wildfires, and changes in infectious agents; sea-level rise and extreme precipitation events are increasing the frequency and intensity of flood events. These climate change impacts have a negative effect on human health, specifically on the most vulnerable populations. Vulnerability is the idea of susceptibility to damage or harm; with respect to climate change, it is a function of exposure, sensitivity, and adaptive capacity. This case study explores the exposure and sensitivity of long-term care facility occupants in British Columbia (B.C.), Canada, because of the high proportion of long-term care residents that are sensitive to climate change. The climate change impacts under review were identified as those with the greatest risk to B.C., the potential to result in significant consequences, as well as current events and prevalence in the region over the past decade. The health effects of these primary climate change impacts were identified through a literature review. Both age and health condition are factors of sensitivity, in B.C. 97% of long-term care facility occupants have chronic diseases (including cardiovascular, endocrine, musculoskeletal, neurological, pulmonary, psychiatric, respiratory, and sensory diseases), and 95% are over the age of 65. A number of chronic diseases (e.g. hypertension and dementia) have been identified that are likely to be exacerbated because of climate change, specifically the four most significant and relevant climate change impacts in B.C.: extreme heat, flooding, changes in infectious agents, and wildfires. In this paper, the proportions of long-term care facility occupants in B.C. with these chronic diseases have been quantified, highlighting the importance of building the adaptive capacity of these populations to decrease their vulnerability. Various building design solutions were explored, confirming the relationship identified in past studies between the built environment, climate change, and occupant health.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".