“I Wish This Tool Was Available to Me Sooner”: Piloting a Workplace Autism Disclosure Decision-Aid Tool for Autistic Youth and Young Adults
Bibliographic record
Abstract
Background: For autistic youth and young adults, deciding whether to disclose their autism at work may be complex since they are newly entering the workforce and are at an impressionable developmental period. Decision-aid tools can help someone make a choice regarding a topic/situation. We developed a workplace autism disclosure decision-aid tool called DISCLOSURE (Do I Start the Conversation and Let On, Speak Up, and REveal?) to support autistic youth and young adults navigate disclosure decision-making. In this study, we aimed to assess the DISCLOSURE tool's (1) impact on decision-making and self-determination capabilities and (2) usability, feasibility, and acceptability. Methods: This was a single-arm pre–post pilot study. The DISCLOSURE tool comprises three interactive PDF documents and videos. Thirty participants (mean age of 23.5 years) completed online surveys before and after interacting with the DISCLOSURE tool. We used descriptive statistics for usability, feasibility, and acceptability. We calculated the Wilcoxon signed rank and paired t -tests to determine pre–post changes in decision-making and self-determination capabilities (Decisional Conflict Scale–Low Literacy Version [DCS-LL]; adapted Arc's Self-Determination Scale). We analyzed open-ended data using conventional (inductive) content analysis. Results: There were significant decreases in DCS-LL total and subscale scores ( p < 0.0001) and a significant increase in Arc's total score ( p = 0.01), suggesting important improvements. There were no significant increases for Arc's psychological empowerment and self-realization subscales ( p = 0.05; p = 0.09). Median scores (4.0/5.0) indicate that participants agreed that the DISCLOSURE tool is acceptable, feasible, and meets the usability criteria. We developed four categories to describe the open-ended data: (1) disclosure capabilities, (2) the role of others, (3) positive tool impact and feedback, and (4) minimal tool impact and constructive feedback. Discussion: Findings are suggestive of the DISCLOSURE tool's ability to support workplace autism disclosure decision-making. Future studies should ascertain the DISCLOSURE tool's effectiveness, explore others' feedback (e.g., employers), and how to incorporate the tool into relevant employment and vocational programs.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".