Self-Rated Health, Life Balance and Feeling of Empowerment When Facing Impacts of Permafrost Thaw—A Case Study from Northern Canada
Bibliographic record
Abstract
Climate warming in Arctic Canada, e.g., permafrost thaw, comprehensively impacts biota and the environment, which then affects the lives of people. This study aimed to investigate which perceived environmental and adaptation factors relate to self-rated well-being, quality of life, satisfaction with life (sum variable = life balance), self-rated health, and feeling of empowerment to face the changes related to permafrost thaw. The study sample was collected from one community using a questionnaire (n = 53) and analyzed by cross-tabulation. Results indicated that most participants had at least good well-being, quality of life, satisfaction with life, and a medium level of health, and over 40% assessed being empowered to face the changes related to permafrost thaw. Problems and challenges associated with permafrost thaw, e.g., health, traditional lifeways, and infrastructure, were recognized; these had impacts on life balance, feeling of empowerment, and self-rated health. Traditional knowledge regarding adaptation to face changes was seen as important. More adaptation actions from the individual to global level seemed to be needed. This study provides an overview of the situation in one area, but more research, with a larger study sample, should be conducted to achieve a deeper understanding of climate-related impacts on life and holistic well-being.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".