Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...
Modeling in Therapy01:26

Modeling in Therapy

Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in situations...
Group Therapy01:26

Group Therapy

Group therapy is a sociocultural approach to psychological treatment, where individuals with shared psychological challenges come together under the guidance of a mental health professional. This therapeutic modality offers unique opportunities for individuals to connect, share, and grow within the context of a supportive group. By fostering mutual understanding and collaboration, group therapy can address a range of psychological concerns effectively, often complementing or surpassing the...
Autism Spectrum Disorder01:19

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Reconstruction of gait variables from 3D foot geometry and sparse plantar pressure landmarks using deep learning.

Journal of biomechanics·2026
Same author

Multiscale heterogeneity of atypical functional connectivity in autism.

Nature. Mental health·2026
Same author

Traumatic Experiences and Major Life Stressors in Children and Youth With Neurodevelopmental Disorders.

Journal of autism and developmental disorders·2026
Same author

An X-linked long non-coding RNA, PTCHD1-AS, and the core features of autism.

Nature·2026
Same author

Use of implementation science models, theories, and frameworks in pediatric rehabilitation: Protocol for a scoping review.

PloS one·2026
Same author

Socio-Demographic Associations and Frequencies of Physical Activity Participation of Autistic Adolescents Reported by Parents in Ontario, Canada.

Journal of autism and developmental disorders·2026
Same journal

A digital marker for stratifying cardiovascular metabolic comorbidities among the middle-aged and elderly adults.

PLOS digital health·2026
Same journal

User engagement in the tuberculosis treatment support tools intervention and its impact on treatment outcomes: A secondary analysis of a pragmatic trial.

PLOS digital health·2026
Same journal

Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania.

PLOS digital health·2026
Same journal

The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study.

PLOS digital health·2026
Same journal

Time-series prediction of adverse birth outcomes in the U.S. using multilayer perceptron neural networks.

PLOS digital health·2026
Same journal

Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective.

PLOS digital health·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 2026

Eye Tracking Young Children with Autism
09:03

Eye Tracking Young Children with Autism

Published on: March 27, 2012

Autism data sharing: Benefits, challenges, and recommendations.

Alexandra Lautarescu1,2, Brett Trost3, Azadeh Kushki4,5

  • 1Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.

PLOS Digital Health
|March 2, 2026
PubMed
Summary
This summary is machine-generated.

Sharing autism research data offers benefits but faces challenges. Autistic people's perspectives are crucial for ethical data sharing, guiding responsible practices and new technologies.

More Related Videos

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research
08:42

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research

Published on: July 31, 2017

Strategies for Assessing Autistic-Like Behaviors in Mice
07:38

Strategies for Assessing Autistic-Like Behaviors in Mice

Published on: September 20, 2024

Related Experiment Videos

Last Updated: Jun 11, 2026

Eye Tracking Young Children with Autism
09:03

Eye Tracking Young Children with Autism

Published on: March 27, 2012

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research
08:42

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research

Published on: July 31, 2017

Strategies for Assessing Autistic-Like Behaviors in Mice
07:38

Strategies for Assessing Autistic-Like Behaviors in Mice

Published on: September 20, 2024

Area of Science:

  • Neuroscience
  • Bioethics
  • Data Science

Background:

  • Data sharing is vital in scientific research but presents significant legal, ethical, and practical hurdles.
  • Autism research data sharing is particularly complex due to concerns about researcher intent, misaligned priorities, and community disagreements.

Purpose of the Study:

  • To review the benefits and challenges of data sharing in autism research.
  • To emphasize the importance of autistic individuals' perspectives in ethical and technological discussions.
  • To propose recommendations for responsible autism data sharing practices.

Main Methods:

  • A collaborative review process involving diverse stakeholders from academia, charity, industry, medicine, and the autism community.
  • Iterative co-production to integrate varied viewpoints.
  • Discussion of existing literature and emerging trends in autism data sharing.

Main Results:

  • Identified key benefits and challenges inherent in autism data sharing.
  • Highlighted the critical need to center autistic people's viewpoints in all data sharing discussions.
  • Acknowledged the emergence of innovative approaches like federated data sharing and community platforms.

Conclusions:

  • Ethical and responsible data sharing in autism research requires the central involvement of autistic individuals.
  • Recommendations are provided for best practices in managing and sharing autism data.
  • Advancements such as federated learning and community registries offer promising avenues for future data sharing initiatives.