A disability disclosure simulation as an educational tool
Bibliographic record
Abstract
Purpose Many employers struggle with how to have a disability disclosure discussion with their employees and job candidates. The primary purpose of this study was to identify issues relevant to disability disclosure discussions. In addition, we explored how simulations, as an educational tool, may help employers and managers. Design/methodology/approach Seven participants (four employers and three human resource professionals) took part in this study. We used a qualitative design that involved two focus group discussions to understand participants' experiences of building a simulation training scenario that focused on how to have a disability disclosure discussion. The simulation sessions were audio-recorded and analyzed using an open-coding thematic approach. Findings Four main themes emerged from our analysis. Three themes focused on issues that participants identified as relevant to the disability disclosure process, including: (1) creating a comfortable and safe space for employees to disclose, (2) how to ask employees or job candidates about disability and (3) how to respond to employees disability disclosure. A fourth theme focused on how simulations could be relevant as an educational tool. Originality/value Developing a simulation on disability disclosure discussions is a novel approach to educating employers and managers that has the potential to help enhance diversity and inclusion in the workplace. Further, the process that we followed can be used as a model for other researchers seeking to develop educational training scenarios on sensitive diversity and inclusion topics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".