Ready2Work: The Development and Evaluation of a User-Informed Online Employment Website for Autistic Job Seekers
Bibliographic record
Abstract
Securing meaningful employment is a priority for many autistic people. In this innovative knowledge translation study, we developed and piloted an online employment platform about the needs of autistic job seekers using feedback from autistic job seekers, caregivers, and employment professionals throughout the process. Development of the online platform unfolded iteratively and based on the feedback provided. In Phase I, we conducted focus groups with 29 participants (7 autistic job seekers, 6 parents, and 16 employment professionals) about employment-related barriers (e.g., concerns with traditional hiring practices). They suggested how the content and design of an online platform could ultimately support employment success for autistic job seekers. The preliminary user-informed website functioned as a resource repository and an active community-maintained section for website members to post and answer employment-related questions, job postings, and related events. In Phase II, we surveyed nine participants (eight autistic job seekers and one caregiver of an autistic job seeker) about their website experiences. We incorporated their feedback into the redesigned website. In Phase III, we asked 14 participants (7 autistic job seekers and 7 supporters of autistic job seekers) to test the redeveloped website. We articulate how the feedback from autistic job seekers, caregivers, and employment professionals was used to develop an online platform. We describe their input and how it was embedded throughout the study, an approach future researchers should prioritize when initiating projects to serve a particular community’s needs.
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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 | 0.011 | 0.024 |
| 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 | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".