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Record W2768652076 · doi:10.1044/persp2.sig2.91

Decolonizing Speech-Language Pathology Practice in Acquired Neurogenic Disorders

2017· article· en· W2768652076 on OpenAlexaff
Claire Penn, Elizabeth Armstrong, Karen Brewer, Barbara Purves, Meaghan McAllister, Deborah Hersh, Erin Godecke, Natalie Ciccone, Abigail Lewis

Bibliographic record

VenuePerspectives of the ASHA Special Interest Groups · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Minority Health and Health DisparitiesUniversity of AucklandUniversity of Pennsylvania
KeywordsAphasiaIndigenousSpeech-Language PathologyTransformative learningRehabilitationDeclarationMedicinePsychologyNursingPolitical sciencePsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

Indigenous peoples throughout the world, despite being known to suffer from increased risk of stroke and traumatic brain injury (TBI), are marginalised in terms of access to rehabilitation services and have poorer health outcomes than non-indigenous peoples. Speech-language pathology services for indigenous people with aphasia have rarely been discussed in either clinical or research fora in this field, with few guidelines available for clinicians when working with indigenous clients, families, and communities. Exploiting the broad input gathered through the collective problem-solving of a focus group, the paper integrates the input of a group of practitioners and researchers at an international roundtable held in 2016 to generate a “declaration” of issues that need to be addressed regarding aphasia services for indigenous clients with aphasia. The paper aims to promote a transformative approach to service delivery that is driven by decolonizing attitudes and practices, and acknowledges historical, sociopolitical, linguistic, and family contexts as a framework for understanding indigenous clients with aphasia.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.053
GPT teacher head0.440
Teacher spread0.387 · 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 designQualitative
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

Citations24
Published2017
Admission routes1
Has abstractyes

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