Energy poverty: an overlooked determinant of health and climate resilience in Canada
Bibliographic record
Abstract
OBJECTIVE: Despite Canada being an important energy producer, not all Canadians can access or afford adequate levels of energy services at home to meet their needs, maintain healthy indoor temperatures, and live a decent life-a situation known as energy poverty. Depending on the measure, 6-19% of Canadian households face energy poverty. Health risks associated with energy poverty are documented in countries with milder climates. This study explores, for the first time in the Canadian context, the association between energy poverty and health. METHODS: Cross-sectional data are from the 2018 Canadian Housing Survey. Analyses are conducted on a sample weighted to represent 14 million Canadian households. The associations between expenditure-based and self-reported measures of energy poverty and self-rated general and mental health were assessed using logistic regression models, adjusted for potential confounding variables. RESULTS: The odds of rating one's general (OR: 1.48; 95%CI: 1.29, 1.70) and mental (OR: 1.21; 1.04, 1.41) health as poor are significantly higher for Canadian adults in households with a high share of energy expenditure to income. The likelihood of poor general and mental health was significantly higher for those dissatisfied with the energy efficiency of their dwelling, and with their ability to maintain a comfortable temperature both in the winter and in the summer. CONCLUSION: Exposure to energy poverty is associated with significantly increased likelihood of poor general and mental health. Given the high proportion of Canadian households facing energy poverty, with demonstrated implications for population health, tackling energy poverty is essential for an equitable energy transition and for climate resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".