The learning needs of the professional.

Neurology

Published: July 1980

The learning needs of the professional in caring for the multiple sclerosis (MS) patient are presented in five categories. The professional needs to know about: the patient, the disease, the other members of the health care team, the patient's supportive network, and the available community resources. A model of patient adjustments to MS is reviewed, and a rationale for the team approach to the care of the MS patient is considered. The concept of care is then expanded to include the role of the family and self-help groups.

Download full-text PDF

Source
http://dx.doi.org/10.1212/wnl.30.7_part_2.55DOI Listing

Publication Analysis

Top Keywords

learning professional
8
professional learning
4
professional caring
4
caring multiple
4
multiple sclerosis
4
patient
4
sclerosis patient
4
patient presented
4
presented categories
4
categories professional
4

Similar Publications

Importance: Rapid digitalization of health care and a dearth of digital health education for medical students and junior physicians worldwide means there is an imperative for more training in this dynamic and evolving field.

Objective: To develop an evidence-informed, consensus-guided, adaptable digital health competencies framework for the design and development of digital health curricula in medical institutions globally.

Evidence Review: A core group was assembled to oversee the development of the Digital Health Competencies in Medical Education (DECODE) framework.

View Article and Find Full Text PDF

Introduction: A multi-stakeholder conference was held in 2023, celebrating the achievements of the Burdett National Transition Nursing Network (BNTNN). The BNTNN had been implemented across England in 2020 to map the current state of young people's healthcare transition into adult services across England, and work with key stakeholders to coach them through making sustainable quality improvements to young people's transition services. This work was funded by the Burdett Trust for Nursing, following the success of an exemplar Model for Quality Improvement (QI) for Transition, which had been developed at a Teaching Hospital in England.

View Article and Find Full Text PDF

Background: This cross-sectional study assessed the global health needs of children aged 2 to 6 years and examined how socio-demographic characteristics influenced children's health needs observed following the COVID-19 pandemic.

Methodology: Cross-sectional study conducted between January and March 2021 in three regions of northern Spain with similar household incomes. Participants were selected through one-stage cluster sampling.

View Article and Find Full Text PDF

Background Artificial Intelligence (AI) is revolutionizing medical science, with significant implications for radiology. Understanding the knowledge, attitudes, perspectives, and practices of medical professionals and residents related to AI's role in radiology is crucial for effective integration. Methods A cross-sectional survey was conducted among members of the Indian Radiology & Imaging Association (IRIA), targeting practicing radiologists and residents across academic and non-academic institutions.

View Article and Find Full Text PDF

AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships.

Comput Struct Biotechnol J

January 2025

Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.

Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!