An ecological systems model of employee experience in industry-led autism employment programmes
Bibliographic record
Abstract
Industry-led employment programmes have emerged to transition autistic people into employment and meet industry labour needs. However, theoretical research is limited in this area, often failing to appreciate the influence of the broader employment ecosystem. In this study, we interviewed 33 autistic employees ( n = 29 males, M age = 29.00 years) from two industry-led employment programmes regarding their experience of the programme’s supports, relationships and impact. We used qualitative content analysis to identify five themes: (1) working involves multiple job tasks that evolve as the employment context changes; (2) workplace relations are diverse and shaped by the type of work and the work environment; (3) workplace needs evolve as autistic individuals navigate the work environment; (4) developing a professional identity in the workplace through mastery and integration; and (5) recommendations for the development of supportive workplace environments for autistic individuals. We describe the way that factors within (e.g. training) and outside the two employment programmes changed and interacted over time to contribute to the participant’s work experience and professional identity. Building on ecological systems theory, our unique contribution to the literature is a new model capturing individual and workplace factors that contribute to the work experience of autistic people who participate in industry employment programmes. Lay Abstract We asked 33 autistic adults from two industry-led employment programmes about their experiences in the programmes. These are programmes started by companies to recruit and support autistic people in work. We also asked about their workplace supports, relationships and how they thought the programme had impacted their life. Understanding the experiences of people in these industry-led employment programmes is important as the information can help to improve the programmes and participants’ experiences. After reviewing the interviews, we found five themes that best described the employee’s experience: (1) working involves multiple job tasks that evolve as the employment context changes; (2) relationships in the workplace are diverse and are influenced by the type of work participants do and the work environment; (3) workplace needs change as the autistic employees learn to navigate their work environment; (4) autistic employees develop a professional identity in the workplace as they master work and feel more integrated in the workplace; and (5) recommendations for the development of supportive workplace environments for autistic people. We explored the way that aspects of the two employment programmes (e.g. training) and factors outside the programme changed with time and contributed to the participant’s experience. We developed a new model to capture individual and workplace factors that contribute to the experience of autistic people who participate in industry employment programmes.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".