Employment Situation and Career Preferences of Persons Who Use Augmentative and Alternative Communication (AAC) in Germany
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".