Connecting with the Earth: Responding to the Impacts of Climate Change on Mental Health in BC Schools
Bibliographic record
Abstract
In this paper, the question of what role school counsellors in BC schools should play to address the multitude of impacts climate change is having on mental health is examined. Clear and extensive links to mental health declines as a result of climate change are made (e.g., Clayton & Manning, 2018; Obradovich, 2018) and focus is put upon vulnerable groups and communities in Canada and British Columbia, school-aged children and youth in particular, to provide recommendations to schools and school counsellors in fostering mental wellbeing as it connects to climate change. The literature review further examines the available research on addressing the impacts of climate change on mental health, with attention brought to Ecopsychology and the practice of Nature-based therapy. Nature-based therapy is surveyed for its potential as both a prevention measure, in particular to cultivate resilience and self-efficacy, as well as an intervention to provide treatment for mood disorders and other impacts upon mental wellbeing connected to both acute and long-term climate change. The professional obligations of school counsellors in BC are called into question, and strong recommendations are made for training and professional development of school counsellors to become climate literate, as well as competent and comfortable in integrating Nature into their counselling practice and school communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".