Disruptions to the delivery of cancer services resulting from climate change: A British Columbia perspective
Bibliographic record
Abstract
Climate change represents a significant challenge to planetary health due to its impacts on ecosystems, biodiversity, and human communities. Extreme climate events are projected to increase in both frequency and severity, including unpredictable rainfall, storms, flooding, heatwaves, droughts, and wildfires. The impacts of these events on individuals’ health, security, and survival are likely to be significant. However, the specific effects of climate change on cancer risk, quality of life, and mortality remain largely unquantified. Climate events are considered an important challenge to the burden on cancer patients because these events cause disruptions in the delivery and quality of care to cancer patients. During 2021, British Columbia (BC) faced two record-breaking weather events. First, during the summer, a ‘heat dome’ occurred over the final ten days of June that caused an excess of 569 deaths. Later in the same year in the southwestern region of BC, severe floods devastated communities and key transportation routes, between November and December. These major climate events have had both substantial effects on individuals’ day-to-day lives and long-term effects for many. These disruptions in healthcare services pose a risk to cancer patients; interruptions in cancer treatment of even one month represents a significant risk of lower quality of life and increased mortality. We have yet to capture the full impact of the specific climate events such as the heat dome and flooding of 2021 on the delivery of cancer services and the corresponding patient outcomes in our province. The climate events that occurred in 2021 showed that further research is urgently needed for developing new protocols and guidelines in the Canadian healthcare system to adapt climate change.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".