Exploration of Gender-Sensitive Care in Vocational Rehabilitation Providers Working With Youth With Disabilities: Codevelopment of an Educational Simulation
Bibliographic record
Abstract
BACKGROUND: Although research shows that there is a need for gender-specific vocational support to help youth with disabilities find employment, health care providers often report needing more training in this area. Currently, there are no existing educational simulations of gender-sensitive care within vocational rehabilitation for clinicians who provide care to youth with disabilities. Therefore, developing further educational tools that address gender-sensitive care could help them enhance the care they provide while optimizing patient outcomes. OBJECTIVE: This study aims to codevelop an educational simulation and identify issues relevant to providing gender-sensitive care within the context of vocational rehabilitation for youth with disabilities. METHODS: We used a qualitative co-design approach with a purposive sampling strategy that involved focus group discussions and journal reflections to understand and address issues relevant to gender-sensitive care within vocational rehabilitation for those working with youth with disabilities. A total of 10 rehabilitation providers participated in two sessions (5 participants per session) to design the web-based simulation tool. The sessions (2.5 hours each) were audio recorded, transcribed, and analyzed thematically. RESULTS: Two main themes arose from our analysis of codeveloping a simulation focusing on gender-sensitive care. The first theme involved the relevance of gender within clinical practice; responses varied from hesitance to acknowledging but not talking about it to those who incorporated gender into their practice. The second theme focused on creating a comfortable and safe space to enable gender-sensitive care (ie, included patient-centered care, effective communication and rapport building, appropriate language and pronoun use, respecting gender identity, awareness of stereotypes, and responding to therapeutic ruptures). CONCLUSIONS: Our web-based gender-sensitive care simulation that addressed vocational rehabilitation among youth with disabilities was cocreated with clinicians. The simulation highlights many issues relevant to clinical practice and has potential as an educational tool for those working with young people with disabilities.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".