Evaluación de resultados personales relacionados con derechos en jóvenes con discapacidad intelectual y Tea

  1. Morales Fernández, Laura 1
  2. Morán Suárez, Lucía 1
  3. Gómez Sánchez, Laura E. 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Journal:
Siglo Cero: Revista Española sobre Discapacidad Intelectual

ISSN: 2530-0350

Year of publication: 2021

Volume: 52

Issue: 3

Pages: 81-99

Type: Article

DOI: 10.14201/SCERO20215238199 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Siglo Cero: Revista Española sobre Discapacidad Intelectual

Abstract

Despite the great importance of the quality of life concept in the intellectual disability (ID) field, literature about its application to youth with autism spectrum disorder (ASD) is scarce, especially for the rights domain, an area that has become particularly important after the ratification of the Convention on the Rights of Persons with Disabilities. This study focuses on assessing the rights of youth with ASD and ID and comparing their results obtained by people with ID and other associated conditions: Down syndrome and cerebral palsy. The Rights subscale from the field-test version of the KidsLife Scale was administered in a sample composed of 153 participants with ID aged from 4 to 21 years old (ASD = 51; Down syndrome = 51; cerebral palsy = 51). The variables gender, type of schooling, level of ID and level of support needs were significant for the group with ASD. The three groups showed positive outcomes, though youth with Down syndrome obtained statistically significant higher scores than participants with ASD.

Bibliographic References

  • S. G. Mallat, "Multifrequency channel decomposition of images and wavelet model", IEEE Transactions on ASSP, Vol. 37, No. 12, pp. 2091-21 10, December 1989.
  • S. 0. Mallat, "A theory for multiresolution signal decomposition: The wavelet representable”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 1 1, No. 7, pp. 674-693, July 1989.
  • Rioul and M. Vetterli, "Wavelets and signal processing", IEEE S. P. magazine, pp. 14-38,October 1991.
  • M. Vetterli and C. Herley, "Wavelets and filter banks: theory and design", IEEE Transactions on Signal Processing, Vol. 40, No. 9, pp. 2207-2232
  • J. Behar, M. Porat and Y. Y. Zeevi, "Image reconstruction from localized phase", IEEE Transactions on Signal Processing, Vol. 40, No. 4, pp. 736-743, April 1992.
  • M. Porat and Y. Y. Zeevi, "Localized texture processing in vision: analysis and synthesis in the Gaborian space", IEEE Transactions on Biomedical Engineering, Vol. 36, No. 1, pp. 1 15- 129, January 1989.
  • M. Antonini, M. Berlaud, P. Mathieu and I. Daubechies, "Image coding using wavelet transform",IEEE Transactions on Image Processing, Vol. 1, No. 2, pp. 205-220, April 1992.
  • T. Chang and C. C. J. Kuo, "Texture analysis and classification with tree-structured wavelet transform", IEEE Transactions on Image Processing, VoL 2, No. 4, pp. 429-440, October 1993.
  • A. Lame and J. Fan, "Texture classification by wavelets packet signatures", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 1 1, pp. 1 186-1 191, March 1992.
  • R. W. Richard, T. Kabir, and F. Liu, “Real-time recognition with the entire Brodatz texture database,” inProc. IEEE Int.Conf. Computer Vision and Pattern Recognition, 1993, pp.683–684.
  • J. Portilla and E. P. Simoncelli, “A parametric texture model based on joint statistics of complex wavelet coefficients,”International Journal of Computer Vision, 2000, to appear.
  • J. Zhang, D. Wang, and Q. N. Tran, “A wavelet-based multiresolution statistical model for texture,”.
  • Bovik,A.Clark , M.Geisler, W.S.,1990. Multichannel texture analysis using localized spatial filters. IEEE .Trans.Pattern.Anal. Machine Intel. 12, 55-73.
  • Unser, M.,1986. Local Linear Transforms for texture measurements. Signal Process 11, 61-79.
  • P. Vautrot, N. Bonnet, and M. Herbin, Comparative study of dierent spatial/spatial-frequency methods (gabor lters, wavelets, wavelet packets)," in IEEE Int. Conf. Im. Proc., vol. 3, 1996, pp. 145{148.
  • N. Fatemi-Ghomi, P.L. Palmer, and M. Petrou, Performance evaluation of texture segmentation algorithms based on wavelets," in Proc. of the workshop on Performance Characteristics of Vision Algorithms, ECCV-96, Cambridge, England, April 1996.
  • A. Laine and J. Fan, Texture classication by wavelet packet signatures," IEEE Trans. Patt. Anal. Mach. Intel l., vol. 15, no. 11, pp. 1186{1190, 1993