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Record W7064674402

Connecting with the Earth: Responding to the Impacts of Climate Change on Mental Health in BC Schools

2021· other· en· W7064674402 on OpenAlexaboutno aff

Bibliographic record

VenueNational University System Repository (National University System) · 2021
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthClimate changePsychological resilienceSchool climateIntervention (counseling)MoodMultitude
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.245
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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