Introduction: Medical faculties worldwide are integrating courses on complementary and alternative medicine (CAM), accepting that knowledge of population's health behaviour, including CAM, increases physicians' ability to communicate, council and treat their patients. The aim of this survey was to assess attitudes towards and knowledge of CAM, and to determine if medical students at the University of Copenhagen perceived a need for education on CAM.

Material And Methods: Self-administered questionnaires were distributed among 508 students on 1st, 2nd, 4th, 6th, 8th and 10th semester. A total of 470 questionnaires were included.

Results: In all, 94% reported knowledge of one or more CAM modalities, 34% reported knowledge of more than five. Most were acquainted with, had tried and would recommend the modalities herbal medicine/supplements, acupuncture and reflexology. Females were more CAM-positive than males and older students were less positive than the aggregate average. The students showed poor knowledge of the general population's use of CAM.

Conclusion: A surprisingly large part of the medical students in Copenhagen reported knowledge and use of CAM compared with other countries, and with the general population, and the students are generally positive towards CAM. The majority agrees that physicians need to possess basic knowledge of CAM, and that courses on CAM should form part of the curriculum.

Download full-text PDF

Source

Publication Analysis

Top Keywords

knowledge cam
16
medical students
12
reported knowledge
12
complementary alternative
8
alternative medicine
8
cam
8
students
7
knowledge
7
[knowledge perceptions
4
perceptions complementary
4

Similar Publications

Background: Polysomnography (PSG) is resource-intensive but remains the gold standard for diagnosing Obstructive Sleep Apnea (OSA). We aimed to develop a screening tool to better allocate resources by identifying individuals at higher risk for OSA, overcoming limitations of current tools that may under-diagnose based on self-reported symptoms.

Methods: A total of 884 patients (490 diagnosed with OSA) were included, which was divided into the training, validation, and test sets.

View Article and Find Full Text PDF

Improving care experiences for premenstrual symptoms and disorders in the United Kingdom (UK): a mixed-methods approach.

BMC Health Serv Res

January 2025

Cambridge Centre for Neuropsychiatric Research, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.

Background: Poor care experiences are reported for premenstrual disorders, which may result in negative outcomes such as distress, reduced healthcare engagement, and delays to diagnosis. This research aimed to explore healthcare experiences for premenstrual symptoms in the United Kingdom and identify areas for potential improvements based on participant responses.

Method: An online survey was delivered, with participants recruited via social media.

View Article and Find Full Text PDF

Background: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.

Objective: This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).

View Article and Find Full Text PDF

Objective: To conduct a systematic review on the masking ability of subtractively and additively manufactured dental ceramics.

Materials And Methods: The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The electronic search was carried out through MEDLINE, Scopus, and Website of Science databases with a date restriction being from 2001 onwards.

View Article and Find Full Text PDF

SEMdag: Fast learning of Directed Acyclic Graphs via node or layer ordering.

PLoS One

January 2025

Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.

A Directed Acyclic Graph (DAG) offers an easy approach to define causal structures among gathered nodes: causal linkages are represented by arrows between the variables, leading from cause to effect. Recently, industry and academics have paid close attention to DAG structure learning from observable data, and many techniques have been put out to address the problem. We provide a two-step approach, named SEMdag(), that can be used to quickly learn high-dimensional linear SEMs.

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!