A Web-Based Gender-Sensitive Educational Simulation on Vocational Rehabilitation for Service Providers Working With Youth With Disabilities: Pilot Evaluation
Bibliographic record
Abstract
BACKGROUND: Although there is a need for gender-specific health care, especially within the context of vocational rehabilitation for youth with disabilities, clinicians, trainees, and community service providers commonly report lacking training in gender-sensitive approaches. Therefore, an educational tool designed for clinicians working with youth, that addresses how to approach such issues, could help clinicians to augment the care they provide. OBJECTIVE: The objective of our study was to conduct a pilot evaluation of an educational simulation for health care and service providers focusing on gender-sensitive approaches within the context of supporting youth with disabilities in vocational rehabilitation. METHODS: We conducted a survey from May to September 2021 to assess the relevance of the simulation content, preliminary perceived impact on gender-sensitive knowledge and confidence, and open-ended feedback of a web-based gender-sensitive educational simulation. A total of 12 health care providers from a variety of professions who had experience working with youth in the context of vocational rehabilitation participated in the survey (11 women and 1 man). RESULTS: Most participants reported that the content of the simulation was relevant and comprehensive. The majority of participants reported that the simulation helped to increase their perceived knowledge or understanding of the topic, changed their perceived understanding of their intervention or approach, and informed their perceived confidence. Our qualitative findings from the open-ended questions highlighted three main themes: (1) relevance of the simulation content, (2) perceived impact for clinical practice (ie, gender-sensitive language and communication and building rapport with patients), and (3) perceived impact on organizational processes (ie, practices, policy, and privacy). CONCLUSIONS: Our educational simulation shows preliminary potential as an educational tool for service providers working with youth who have a disability within the context of vocational rehabilitation. Further research is needed to assess the impact of the tool with larger samples.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".