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Record W2129087903 · doi:10.1177/0008417413520441

The cultural brokerage work of occupational therapists in providing culturally sensitive care

2014· article· en· W2129087903 on OpenAlexvenueaboutno aff
Sally Lindsay, Sylvie Tétrault, Chantal Desmaris, Gillian King, Gènevive Piérart

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyCultural diversityRehabilitationNursingImmigrationCultural competenceDiversity (politics)Health careQualitative researchPsychologyWork (physics)Culturally sensitiveCultural issuesMedicineSociologySocial psychologyPsychiatryPedagogyPolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: The important place of culture within occupational therapy is widely recognized, and there is increasing emphasis on addressing the diversity of clients. PURPOSE: This study explores how occupational therapists perform cultural brokerage when providing culturally sensitive care to immigrant families. METHOD: A descriptive qualitative methodology was used for this study. A purposive sample of 17 occupational therapists from two Canadian paediatric rehabilitation centres were interviewed. FINDINGS: Participants encountered several cultural and structural constraints in providing culturally sensitive care. To overcome these constraints, clinicians used four strategies: (a) translating between health systems for clients, (b) bridging different meanings of occupational therapy to make it relevant for clients, (c) establishing long-term relationships by building trust and rapport, and (d) working with clients' relational networks to help them navigate the health system. IMPLICATIONS: Occupational therapists should advocate for both the individual needs of immigrant families and for institutional level resources to better meet the needs of diverse clients.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.214
GPT teacher head0.490
Teacher spread0.276 · 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 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

Citations41
Published2014
Admission routes2
Has abstractyes

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