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Stress management in dental students: a systematic review.

ABSTRACT: This study compared the effectiveness of stress management programs in dental education by systematic review of the literature. The number of studies concerning stress management programs for dental students is limited compared with studies discussing sources of stress. Several types of programs for stress management have been reported, and differ in their duration, content, and outcomes. Two main strategies have been used to help stressed students, ie, decreasing the number of stressors and increasing the ability to cope with stress. The first strategy includes several components, such as reducing fear of failure and workload pressure due to examinations and requirements. The second strategy includes coping techniques, such as deep breathing exercises. Although positive effects have been reported for most of the programs, these have mainly been evaluated using subjective self-report measures. There is a need for more research to identify the most effective stress management program.

SUBMITTER: Alzahem AM

PROVIDER: S-EPMC4041220 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Stress management in dental students: a systematic review.

Alzahem Abdullah M AM Van der Molen Henk T HT Alaujan Arwa H AH De Boer Benjamin J BJ

Advances in medical education and practice 20140528

This study compared the effectiveness of stress management programs in dental education by systematic review of the literature. The number of studies concerning stress management programs for dental students is limited compared with studies discussing sources of stress. Several types of programs for stress management have been reported, and differ in their duration, content, and outcomes. Two main strategies have been used to help stressed students, ie, decreasing the number of stressors and inc ...[more]

PMID: 24904226

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Project description:BackgroundDental practitioners and dental students are classified as high-risk exposure to COVID-19 due to the nature of dental treatments, but evidence of their acceptance towards COVID-19 vaccination is still scarce. Hence, this systemic review aims to critically appraise and analyse the acceptability of COVID-19 vaccination among dental students and dental practitioners.Materials and methodsThis review was registered in the PROSPERO database (CRD42021286108) based on PRISMA guidelines. Cross-sectional articles on the dental students' and dental practitioners' acceptance towards COVID-19 vaccine published between March 2020 to October 2021 were searched in eight online databases. The Joanna Briggs Institute critical appraisal tool was employed to analyse the risk of bias (RoB) of each article, whereas the Oxford Centre for Evidence-Based Medicine recommendation tool was used to evaluate the level of evidence. Data were analysed using the DerSimonian-Laird random effect model based on a single-arm approach.ResultsTen studies were included of which three studies focused on dental students and seven studies focused on dental practitioners. Four studies were deemed to exhibit moderate RoB and the remaining showed low RoB. All the studies demonstrated Level 3 evidence. Single-arm meta-analysis revealed that dental practitioners had a high level of vaccination acceptance (81.1%) than dental students (60.5%). A substantial data heterogeneity was observed with the overall I2 ranging from 73.65% and 96.86%. Furthermore, subgroup analysis indicated that dental practitioners from the Middle East and high-income countries showed greater (p < 0.05) acceptance levels, while meta-regression showed that the sample size of each study had no bearing on the degree of data heterogeneity.ConclusionsDespite the high degree of acceptance of COVID-19 vaccination among dental practitioners, dental students still demonstrated poor acceptance. These findings highlighted that evidence-based planning with effective approaches is warranted to enhance the knowledge and eradicate vaccination hesitancy, particularly among dental students.

Project description:PurposeThe study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain.MethodScopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.Results34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.ConclusionMachine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.

Dataset Information

Stress management in dental students: a systematic review.

Publications

Stress management in dental students: a systematic review.

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