Sustainability Modelling for Employment-focused Training Ecosystems for Young Adults with Disabilities
Bibliographic record
Abstract
Background: Neurodivergent young adults face significant employment challenges globally, with unemployment rates reaching 80% in India. This study examines an innovative employment-focused training ecosystem for neurodivergent individuals, incorporating technological interventions and a gig economy model. Neurodivergent individuals are those whose brain functions differently in one or more ways than is considered standard or typical. Methods: A mixed-methods approach was employed, combining quantitative analysis of program outcomes with qualitative insights from stakeholders. The study utilized technology interventions for skill assessment, implemented a 5D clarity process-based training curriculum, and integrated a gig economy framework. Results: The study demonstrated notable success in employment outcomes, with a significant proportion of participants securing work within months of completion. Participants reported substantial gains in digital skills acquisition. Technological interventions for assessments revealed unique strengths in individuals that were not apparent through traditional methods. The gig economy model showed promise in providing flexible, suitable employment options for neurodivergent individuals. Conclusions: The innovative ecosystem demonstrates significant potential in creating sustainable employment opportunities for neurodivergent individuals, addressing key gaps in traditional training and employment models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".