Game-based assessments vs interviews : interview anxiety and performance among individuals with and without ASD
Bibliographic record
Abstract
In the workplace, a substantial portion of life is dedicated to work, offering sustenance, social connections, and individual identity. However, individuals with autism spectrum disorder (ASD) face significant underemployment globally despite possessing various skills. Traditional interview-based selection methods often create systematic bias against ASD individuals due to social skill deficits and heightened anxiety. This study contrasts three assessment approaches: asynchronous video interviews (AVI), video conference interviews (VCI), and game-based assessments (GBA). The primary aim is to mitigate bias using GBA. Anxiety levels were measured with an adapted version of the Measure of Anxiety in Selection Interviews (MASI). A mixed ANOVA assessed performance and anxiety differences between ASD and non-ASD groups across the three assessment types. Results showed no significant differences in anxiety levels across assessment types for both groups. Performance results indicated individuals with ASD performed significantly lower in VCIs compared to non-ASD individuals but better in AVIs and GBAs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".