Responding to a Changing Climate: An Investigation of the Psychosocial Consequences of Climate Change and Community-based Mental Health Responses in High River
Bibliographic record
Abstract
This dissertation explores the psychosocial consequences of climate change and psychosocial adaptation opportunities in High River, Alberta. Influenced by the theoretical approach of Political Ecology, I investigate community impacts and community-based mental health responses in High River following the 2013 Southern Alberta floods. Research methods include: a desktop climate change and health vulnerability and adaptation assessment that includes over 116 data sources; telephone interviews with key informant health and social services leaders (n = 14); four focus group sessions with front-line health and social services workers (n = 14); and, semi-structured interviews (n = 18) with a sample of community-members exposed to the 2013 flood and who self-identify in any one or more ways: female, youth, elderly, non-white, someone living in a low socio-economic status, someone with pre-existing health concerns. A total of 46 participants were recruited in this research. Results of the empirical investigation in High River are showcased in three manuscripts. The first manuscript, informed by critical Political Ecology, is an investigation of sociopolitical conditions that influence health inequities and adaptation opportunities (or lack thereof) in a changing climate in High River. The second is an empirical exploration of the long-term psychosocial consequences of the 2013 flood, relating these consequences to the broader issue of climate change and psychosocial health. The third manuscript is an exploration of how the long-term psychosocial risks and impacts of the 2013 flood are being addressed via response interventions and connecting these lessons-learned to the broader topic of climate change and psychosocial adaptation. This research suggests that a Political Ecology lens can support a critical analysis of what the environment means and to whom, with a particular focus on what adaptation to our changing climate means and to whom. Further, findings from this empirical research suggest that there are a range of psychosocial outcomes related to the 2013 Southern Alberta flood, and these outcomes continue to affect the psychosocial wellbeing of High River residents, particularly those most marginalized, five years post-flood. Findings also suggest that there are lessons-learned from High River about actions to support or enhance psychosocial adaptation to a changing climate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".