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Record W4382798039 · doi:10.1080/14473828.2023.2219476

Case study: Benefits and challenges of automated assistive technology devices for spinal cord-injured citizens in Greece

2023· article· en· W4382798039 on OpenAlexaboutno aff
Maria-Loreta Miskala, Panagiotis Siaperas

Bibliographic record

VenueWorld Federation of Occupational Therapists Bulletin · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsAssistive technologyOccupational therapyScope (computer science)Intervention (counseling)Spinal cord injuryAssistive deviceProcess (computing)Applied psychologyMedicinePsychologyPhysical medicine and rehabilitationMedical educationNursingPhysical therapyComputer scienceSpinal cordHuman–computer interactionPsychiatry

Abstract

fetched live from OpenAlex

The purpose of the present case study was to investigate the impact of assistive technology on the engagement in and performance of preferred occupations, for spinal cord-injured individuals in Greece. Occupational therapists, uniquely qualified to address issues of occupational justice, considering assistive technology may need to broaden their scope, focusing on a holistic approach. Having compiled an occupational profile of the case study, using the semi-structured interviews of the Canadian Occupational Performance Measure and the Quebec User Evaluation of Satisfaction with Assistive Technology, a better understanding of the matter was possible. People with spinal cord injury face not only issues of functionality but also environmental and social barriers, regardless of the device they use. Assistive technology devices may have positive outcomes for people with physical disabilities. However, Greek service users may greatly benefit from a more structured, person-centered and occupational-focused assessment, provision, accessibility and intervention process to optimize AT benefits.

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)0.000
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 score0.217
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.215
GPT teacher head0.472
Teacher spread0.257 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueWorld Federation of Occupational Therapists BulletinSame topicAssistive Technology in Communication and MobilityFrench-language works237,207