Weathering the Storm: Climate‐Related Weather Event Experiences of Families of Children With Medical Complexity
Bibliographic record
Abstract
BACKGROUND: Climate change is increasingly an urgent concern because of the catastrophic and irreversible impacts on the planet and society as a whole. In recent years, there have also been more frequent and extreme weather events such as heatwaves, droughts, floods and wildfires across the globe. Although families of children with medical complexity may be particularly vulnerable to the impacts of extreme weather events, few studies exist that address this topic. METHODS: Drawing upon a subset of data from two qualitative studies, we explored the experiences of families during climate-related weather events in British Columbia, Canada. During the summer and fall of 2021, this area experienced a series of extreme weather events (e.g., heat dome, wildfires and atmospheric rivers causing flooding) in rapid succession. Semistructured interviews were conducted with 30 parents between July 2021 and April 2022. Descriptive content analysis was used for data analysis. RESULTS: Participants described their lived experiences during the heat dome, wildfires and flooding of 2021. Across all of the weather events, children with medical complexity experienced social isolation and, for some, increased anxiety. Participants also shared adaptive measures, or strategies, they used during these events. In the absence of government supports, families drew upon their informal peer networks in some situations. CONCLUSIONS: The unique needs of children with medical complexity are not accounted for in existing government policies and supports related to climate-related weather events. Families are self-reliant and draw upon their informal peer network for supports. There is an urgent need for inclusive programs and supports across emergency management, health, education and social care to address the needs of this group.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".