scholarly journals The integration of psychology in pediatric oncology research and practice: Collaboration to improve care and outcomes for children and families.

2015 ◽  
Vol 70 (2) ◽  
pp. 146-158 ◽  
Author(s):  
Anne E. Kazak ◽  
Robert B. Noll
2002 ◽  
Vol 19 (2) ◽  
pp. 72-73
Author(s):  
Jami S. Gattuso ◽  
Elizabeth A. Gilger ◽  
Georgette Chammas ◽  
Samuel Maceri ◽  
Nancy K. West ◽  
...  

2021 ◽  
Vol 41 (1) ◽  
pp. 18-30
Author(s):  
Xigrid T. Soto-Boykin ◽  
Anne L. Larson ◽  
Arnold Olszewski ◽  
Veena Velury ◽  
Anna Feldberg

Young children with and without disabilities who are bilingual or in the process of learning multiple languages have many strengths; however, educational policies and bias related to bilingualism for children from linguistically minoritized groups have typically included deficit-based views. The purpose of this systematic review was to identify how researchers describe these children and their caregivers. Thirty research studies were included in the review. Each study was published in Infants and Young Children, Journal of Early Intervention, or Topics in Early Childhood Special Education between 1988 and 2020. Studies were coded to determine participant characteristics and whether deficit- or strength-based descriptions of participants were used. Although researchers’ descriptions of participants’ linguistic backgrounds varied, most were English-centric, and deficit-based descriptions of bilingualism were more prevalent than strength-based descriptions. Preliminary recommendations are provided for describing children and families from linguistically minoritized communities and including strength-based language in research and practice.


2018 ◽  
Vol 7 (1) ◽  
pp. e2 ◽  
Author(s):  
Cynthia Chaput ◽  
Sabrina Beaulieu-Gagnon ◽  
Véronique Bélanger ◽  
Simon Drouin ◽  
Laurence Bertout ◽  
...  

2020 ◽  
pp. 799-810
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.


Sign in / Sign up

Export Citation Format

Share Document