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Record W4243024123 · doi:10.1080/17483107.2018.1468496

Assistive technology policy: a position paper from the first global research, innovation, and education on assistive technology (GREAT) summit

2018· article· en· W4243024123 on OpenAlexaff
Malcolm MacLachlan, David Banes, Diane Bell, Johan Borg, Brian Donnelly, Michael Fembek, Ritu Ghosh, Rosemary Joan Gowran, Emma Hannay, Diana Hiscock, Evert-Jan Hoogerwerf, Tracey Howe, Friedbert Köhler, Natasha Layton, Siobhán Dowling Long, Hasheem Mannan, Gubela Mji, Thomas Odera Ongolo, Kyle D. Perry, Cecilia Pettersson, J. R. Power, Vinícius Delgado Ramos, Lenka Slepičková, Emma Smith, Kiu Tay-Teo, Priscille Geiser, Hilary Hooks

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSummitPublic relationsPosition (finance)Political sciencePosition paperProcess (computing)Assistive technologyBusinessEconomic growthKnowledge managementMedicineComputer scienceEconomics

Abstract

fetched live from OpenAlex

Increased awareness, interest and use of assistive technology (AT) presents substantial opportunities for many citizens to become, or continue being, meaningful participants in society. However, there is a significant shortfall between the need for and provision of AT, and this is patterned by a range of social, demographic and structural factors. To seize the opportunity that assistive technology offers, regional, national and sub-national assistive technology policies are urgently required. This paper was developed for and through discussion at the Global Research, Innovation and Education on Assistive Technology (GREAT) Summit; organized under the auspices of the World Health Organization's Global Collaboration on Assistive Technology (GATE) program. It outlines some of the key principles that AT polices should address and recognizes that AT policy should be tailored to the realities of the contexts and resources available. AT policy should be developed as a part of the evolution of related policy across a number of different sectors and should have clear and direct links to AT as mediators and moderators for achieving the Sustainable Development Goals. The consultation process, development and implementation of policy should be fully inclusive of AT users, and their representative organizations, be across the lifespan, and imbued with a strong systems-thinking ethos. Six barriers are identified which funnel and diminish access to AT and are addressed systematically within this paper. We illustrate an example of good practice through a case study of AT services in Norway, and we note the challenges experienced in less well-resourced settings. A number of economic factors relating to AT and economic arguments for promoting AT use are also discussed. To address policy-development the importance of active citizenship and advocacy, the need to find mechanisms to scale up good community practices to a higher level, and the importance of political engagement for the policy process, are highlighted. Policy should be evidence-informed and allowed for evidence-making; however, it is important to account for other factors within the given context in order for policy to be practical, authentic and actionable. Implications for Rehabilitation The development of policy in the area of asssitive technology is important to provide an overarching vision and outline resourcing priorities. This paper identifies some of the key themes that should be addressed when developing or revising assistive technology policy. Each country should establish a National Assistive Technology policy and develop a theory of change for its implementation.

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)0.018
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.048
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0100.007
Scholarly communication0.0240.014
Open science0.0030.007
Research integrity0.0200.015
Insufficient payload (model declined to judge)0.0070.002

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 head0.044
GPT teacher head0.444
Teacher spread0.400 · 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 designNot applicable
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

Citations181
Published2018
Admission routes1
Has abstractyes

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