Quality of Life in Higher Education for Individuals with Intellectual Disabilities and Family Perceptions
Bibliographic record
Abstract
Background: Intellectual disability is a disorder of intellectual development that, due to its characteristics, involves a series of limitations in cognitive functioning and generalization of learning. These difficulties can evolve with appropriate intervention, which can enhance the quality of life of these people and their families. Objectives: The study seeks to determine the quality of life of people with intellectual disabilities in general and in its different dimensions, taking into account the perception of the person with a disability and that of his/her family. Methods: This study focuses on a specific field: higher education for people with intellectual disabilities. It uses a sample of 19 young people and their 19 corresponding families to determine whether there are significant differences between the two groups, i.e., parents and children. The INICO-FEAPS tool was used to achieve these objectives. Results: The data obtained, and their subsequent analysis have allowed us to detect medium-high levels of quality of life and significant differences between the indices of both groups, highlighting higher scores in the case of families. In addition, self-determination has been revealed as the dimension with the lowest scores, followed by social inclusion and interpersonal relationships. Conclusion: This makes it possible, at the professional level, to focus on these aspects and become aware of the necessary measures to improve intervention in the educational and family environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".