The Effectiveness of a Training Program in Improving the Competencies and Skills of Female Specialists to Work with People with Visual Impairments
Bibliographic record
Abstract
This study aimed to evaluate the effectiveness of a training program in improving the competencies and skills of female specialists working with people with visual impairments and to determine if there were any statistically significant differences in the improvement of competencies and skills due to variables such as specialization, years of experience, prior knowledge of a blind person, and training courses in the field of visual impairment. The study sample consisted of 10 female specialists from special education, visual impairment, and early childhood in Riyadh, Saudi Arabia. A 70-item scale was used to measure the competencies and skills of female specialists before and after the training program. The scale was divided into five domains: knowledge of visual impairments and their impact on learning, communication and interaction strategies, adaptation of teaching materials and classroom environment, assessment and evaluation strategies, and collaboration and professional development. The study's results revealed a significant improvement in the competencies and skills of female specialists after participating in the training program, with an average percentage improvement of 41.4%. No statistically significant differences were found in the improvement of competencies and skills due to the variables of specialization, years of experience, prior knowledge of a blind person, and training courses in the field of visual impairment. In conclusion, the training program was found to be effective in improving the competencies and skills of female specialists working with people with visual impairments. The results highlight the importance of providing specialized training to professionals in this field to support better the learning and development of children with visual impairments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".