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Record W3008947554 · doi:10.1080/14473828.2020.1727095

Building occupational therapy practice ecological based occupations and ecosystem sustainability: exploring the concept of eco-occupation to support intergenerational occupational justice

2020· article· en· W3008947554 on OpenAlexaff
Yannick Ung, Thiébaut Samson Sarah, Marie-Josée Drolet, Salvador Simó Algado, Muriel Soubeyran

Bibliographic record

VenueWorld Federation of Occupational Therapists Bulletin · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsOccupational therapySustainabilitySociologyPopulationEconomic JusticeEngineering ethicsIdentity (music)HumanismOccupational scienceMeaning (existential)Environmental ethicsPublic relationsPsychologyPolitical scienceEcologyPsychotherapistEngineering

Abstract

fetched live from OpenAlex

This article proposes that occupational therapists must take on the role of agents of change in order to act in an eco-responsible way towards the population. Our recent literature review explores the critique of the foundations of the occupational therapist profession through the perspective of professional practices focused on eco-systems, and its respect rather than its exploitation. Several concepts have been defined in order to support occupational therapists in understanding the current and future challenges of populations, especially those most vulnerable to the climate and ecological crisis. These concepts thus aim to maintain the balance of ecosystems, promote eco-occupations and support intergenerational occupational justice. It is therefore important for us, occupational therapists, to be rooted in eco-humanist values, to be aware of the real role we face as citizens of the world. It is also about engaging us to build on our professional identity to give meaning to our new professional activities and support eco-social and sustainable occupational therapy.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.146
GPT teacher head0.380
Teacher spread0.234 · 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 designObservational
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

Citations19
Published2020
Admission routes1
Has abstractyes

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