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Record W4319762215 · doi:10.4236/jss.2023.112005

Employment Situation and Career Preferences of Persons Who Use Augmentative and Alternative Communication (AAC) in Germany

2023· article· en· W4319762215 on OpenAlexaff
Gregor Renner, Dustin Karl, Beata Batorowicz

Bibliographic record

VenueOpen Journal of Social Sciences · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsQueen's University
Fundersnot available
KeywordsAugmentative and alternative communicationPsychologyPerspective (graphical)Applied psychologySocial psychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Purpose: Little is known about employment situations as well as career preferences and aspirations of people who use Augmentative and Alternative Communication (AAC), especially from the perspective of these individuals themselves. The purpose of this study was therefore to explore 1) employment situations of the persons who use AAC; 2) their satisfaction with the employment situation; and 3) their career preferences. Methods: An online questionnaire was developed for the purpose of this study. Twenty-one persons, aged 16 to 65, participated in this study. Results: Ten participants (47.6%) were employed in disability specific workshops, five (23.8%) attended adult day centres for people with disabilities, two (9.5%) were unemployed, two (9.5%) were students, and two (9.5%) did not specify their employment status. Nine participants (42.9%) were satisfied with their current employment situation to some extent, while another nine were not satisfied (42.9%). Out of 21 participants, 15 (71.4%) desired a change of their situation, of which nine (42.9%) aspired to employment in the general labour market. Conclusions: Future research is needed to address specific barriers and facilitators related to accessing meaningful employment for individuals who rely on AAC and accommodations needed to support such employment.

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)0.002
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 score0.062
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.340
GPT teacher head0.526
Teacher spread0.186 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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