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Record W2103025832 · doi:10.3109/07434618.2015.1030038

Towards Advancing Knowledge Translation of AAC Outcomes Research for Children and Youth with Complex Communication Needs

2015· article· en· W2103025832 on OpenAlexafffund
Stephen E. Ryan, Tracy A. Shepherd, Anne Marie Renzoni, Colleen Anderson, Mary I. Barber, Shauna Kingsnorth, Karen Ward

Bibliographic record

VenueAugmentative and Alternative Communication · 2015
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAugmentative and alternative communicationKnowledge translationLeverage (statistics)Medical educationLiteracyPsychologyKnowledge managementComputer scienceMedicinePedagogy

Abstract

fetched live from OpenAlex

The production of new knowledge in augmentative and alternative communication (AAC) requires effective processes to leverage the different perspectives of researchers and knowledge users and improve prospects for utilization in clinical settings. This article describes the motivation, planning, process, and outcomes for a novel knowledge translation workshop designed to influence future directions for AAC outcomes research for children with complex communication needs. Invited knowledge users from 20 pediatric AAC clinics and researchers engaged in the collaborative development of research questions using a framework designed for the AAC field. The event yielded recommendations for research and development priorities that extend from the early development of language, communication, and literacy skills in very young children, to novel but unproven strategies that may advance outcomes in transitioning to adulthood.

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)0.251
metaresearch head score (Gemma)0.299
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.251
Threshold uncertainty score0.924

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2510.299
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0050.008
Scholarly communication0.0110.014
Open science0.0040.021
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0070.001

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 head0.441
GPT teacher head0.562
Teacher spread0.121 · 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.

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

Citations16
Published2015
Admission routes2
Has abstractyes

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