Autistic-inclusive employment: A qualitative interpretive meta-synthesis
Bibliographic record
Abstract
Abstract Inclusive employment is a key topic and area of practice for social workers. Unfortunately, autistic adults face multiple barriers to accessing and retaining employment. There is much to be learned through reflection on the employment experiences of autistic individuals to gain a more nuanced insight on the phenomenon of autistic-inclusive employment. Informed by critical disability and neurodiversity scholarship, a qualitative interpretive meta-analysis was conducted to review and interpret autistic adults’ experiences with employment and offer a translational understanding of autistic-inclusive employment for key stakeholders. Eleven (N = 11) qualitative studies utilizing inclusive research design and representing 632 participants were reviewed and synthesized through the lens of inclusive employment. This process resulted in four key themes that describe autistic adults’ experiences with autistic-inclusive employment: (1) organizational culture, (2) workplace environment, (3) disclosure and accommodations, and (4) role alignment. Findings from this study highlight opportunities to enhance autistic-inclusive workplace policies and practices. Relevant implications for social workers, employers, researchers, policy makers, and autism advocates are shared.
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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 | 0.126 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".