Efforts and Effectiveness in Improving Knowledge and Skills of Vocational Assessment for Teachers Supporting Career Decisions of Students with Intellectual Disabilities
Bibliographic record
Abstract
Background: The number of options available to people with disabilities has gradually increased in Japan. At educational institutions where students with disabilities are enrolled, teachers must understand the various ways of life and provide appropriate guidance and support based on the actual conditions of individual students so that students can independently choose their career paths through self-selection and self-determination. Objective: This study aimed to examine the effect of an intervention using a vocational assessment training program on eight teachers at a special needs school in Japan. Methods: The level of knowledge and skills was assessed before and after the intervention. Intervention effects were analyzed through the Self-Assessment for Students and Counselors-Revised tool using the Wilcoxon signed-rank test, with corresponding samples for differences in mean scores before and after the intervention. In addition, the difference in the mean pre- and post-intervention scores for teachers with 10 or more years of upper secondary school experience (high experience group) and those with less than 10 years of upper secondary school experience (low experience group) was calculated. Principal Verdicts: Indicated that the training program intervention helped teachers recognize the importance of vocational assessment and subsequently improve their teaching. Furthermore, the number of years of experience in upper secondary schools influenced the effectiveness of the intervention. Conclusions: It was suggested that this training program would be effective for professionals who have acquired basic knowledge and skills in vocational rehabilitation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".