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Record W4404375653 · doi:10.26443/mjgh.v13i1.1360

How Can Occupational Therapists Contribute to Climate Action?

2024· article· en· W4404375653 on OpenAlexaffabout
Naomi Laflamme

Bibliographic record

VenueMcGill Journal of Global Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsAction (physics)Occupational therapyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Climate change can have many devastating effects on health and disrupt occupations, including activities of work, self-care, and leisure. Thus, occupational therapists (OTs) have a role to play in climate action by promoting sustainable occupational therapy practices and educating clients on the importance of employing more eco-responsible occupations. However, within the Canadian context, the OTs who support climate action may face many difficulties when advocating for it. This analytical essay will explore the multiple barriers to the implementation of sustainable practices in Canada that OTs encounter. As most Canadian citizens adopt consumerist lifestyles due to Western ideals and systems, this can affect clients’ and professionals’ receptibility to sustainability education and motivation. Moreover, many Canadian OTs lack education on climate action in their profession, and current resources are either overwhelming or unclear. Nonetheless, Canadian OT leadership, specifically the CAOT, has begun taking initiative, thus while work is still in progress, the outcomes have yet to appear, however there are great hopes for the future. In conclusion, while there are still many barriers to overcome, OTs have a great potential to become active change agents in the fight against climate change by collaborating with clients and colleagues alike to spread awareness and build sustainability in occupations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.142
GPT teacher head0.546
Teacher spread0.404 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes2
Has abstractyes

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