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Record W6901742361 · doi:10.60692/zy03w-st726

Stitching a new garment: Considering the future of the speech–language therapy profession globally

2022· article· en· W6901742361 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsDalhousie University
Fundersnot available
KeywordsViewpointsTerminologyEquity (law)Service delivery frameworkFocus groupProcess (computing)Cultural humilityService providerGlobal citizenship

Abstract

fetched live from OpenAlex

Providing equitable support for people experiencing communication disability (CD) globally is a historical and contemporary challenge for the speech-language therapy profession. A group of speech-language therapists (SLTs) with ongoing and sustained experiences in Majority and Minority World contexts participated in five virtual meetings in 2021. The aim of these meetings was to develop provocative statements that might spur a global discussion among individuals and organisations that support people experiencing CD. The following questions were discussed: What is our vision for the future of the profession globally? What are the global challenges around access to speech-language therapy services?Four main themes emerged: (1) the need to centre people experiencing CD as the focal point of services, (2) participation, (3) equity and (4) community. The themes relate to the need for a process of de-imperialism in the profession. Suggestions were made to develop more suitable terminology and to establish a global framework that promotes more equitable access to communication services. We seek the adoption of approaches that focus on reciprocal global engagement for capacity strengthening. Alternative models of culturally sustaining and equitable service delivery are needed to create impact for people experiencing CD, and their families worldwide.Contribution: Provocative statements were developed to prompt global conversations among speech-language therapy professionals and associations. We encourage readers to consider the questions posed, share their viewpoints and initiate positive change towards a global strategy.

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)0.017
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0230.019
Scholarly communication0.0170.021
Open science0.0020.019
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0060.001

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 head0.059
GPT teacher head0.337
Teacher spread0.279 · 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
GenreCommentary

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

Citations0
Published2022
Admission routes1
Has abstractyes

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