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Record W2029156580 · doi:10.1080/09638280902773711

The Kawa model: The power of culturally responsive occupational therapy

2009· article· en· W2029156580 on OpenAlexaffabout
Michael K. Iwama, Nicole Thomson, Rona M. Macdonald

Bibliographic record

VenueDisability and Rehabilitation · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRehabilitationTransactional leadershipMetaphorOccupational therapyQuality (philosophy)PsychologyReflection (computer programming)SociologyEpistemologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

The Kawa (Japanese for river) model, developed by Japanese and Canadian rehabilitation professionals, presents an important and novel alternative to contemporary 'Western' models of rehabilitation. Rather than focussing primarily on the individual client, the Kawa model focusses on 'contexts' that shape and influence the realities and challenges of peoples' dayto-day lives. The first substantial model of rehabilitation practice developed outside of the West illuminates the transactional quality of human-environment dynamics and the importance of inter-relations of self and others through the metaphor of a river's flow. The model's reflection of Eastern thought and views of nature presents a useful point of comparison to familiar rational and mechanical explanations of occupation and well-being. In this article, the rationale for an alternative model in rehabilitation is presented, followed by an explanation of the structure and concepts of the Kawa model. Implications for culturally responsive practice as well as the model's significance to the advancement of culturally safe rehabilitation worldwide are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.040
Scholarly communication0.0110.009
Open science0.0020.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.078
GPT teacher head0.479
Teacher spread0.401 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations123
Published2009
Admission routes2
Has abstractyes

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