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"@type": "Question",
"name": "Comment évaluer la communication interprofessionnelle ?",
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"text": "Utiliser des outils d'évaluation comme des questionnaires et des entretiens."
}
},
{
"@type": "Question",
"name": "Quels indicateurs mesurent la collaboration interprofessionnelle ?",
"position": 2,
"acceptedAnswer": {
"@type": "Answer",
"text": "Les indicateurs incluent la satisfaction des patients et l'efficacité des soins."
}
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{
"@type": "Question",
"name": "Quels outils facilitent le diagnostic interprofessionnel ?",
"position": 3,
"acceptedAnswer": {
"@type": "Answer",
"text": "Les outils incluent des plateformes de partage d'informations et des réunions d'équipe."
}
},
{
"@type": "Question",
"name": "Comment identifier les barrières à la collaboration ?",
"position": 4,
"acceptedAnswer": {
"@type": "Answer",
"text": "Par des enquêtes et des discussions en groupe pour recueillir des retours d'expérience."
}
},
{
"@type": "Question",
"name": "Quel rôle joue la culture organisationnelle dans le diagnostic ?",
"position": 5,
"acceptedAnswer": {
"@type": "Answer",
"text": "Une culture ouverte favorise la communication et la collaboration entre les professionnels."
}
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{
"@type": "Question",
"name": "Quels symptômes indiquent une mauvaise communication ?",
"position": 6,
"acceptedAnswer": {
"@type": "Answer",
"text": "Des erreurs médicales fréquentes et des plaintes des patients peuvent en être des signes."
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{
"@type": "Question",
"name": "Comment reconnaître un manque de collaboration ?",
"position": 7,
"acceptedAnswer": {
"@type": "Answer",
"text": "Des conflits fréquents et un manque de coordination dans les soins sont des indicateurs."
}
},
{
"@type": "Question",
"name": "Quels symptômes affectent la dynamique d'équipe ?",
"position": 8,
"acceptedAnswer": {
"@type": "Answer",
"text": "Le stress, la frustration et le turnover élevé peuvent affecter la dynamique d'équipe."
}
},
{
"@type": "Question",
"name": "Quels signes montrent une bonne collaboration ?",
"position": 9,
"acceptedAnswer": {
"@type": "Answer",
"text": "Une communication fluide et des résultats positifs pour les patients sont des signes clés."
}
},
{
"@type": "Question",
"name": "Comment les symptômes de stress impactent-ils les relations ?",
"position": 10,
"acceptedAnswer": {
"@type": "Answer",
"text": "Le stress peut mener à des conflits et à une communication inefficace entre les professionnels."
}
},
{
"@type": "Question",
"name": "Comment prévenir les conflits interprofessionnels ?",
"position": 11,
"acceptedAnswer": {
"@type": "Answer",
"text": "Par des formations régulières sur la communication et la gestion des conflits."
}
},
{
"@type": "Question",
"name": "Quelles stratégies améliorent la prévention des erreurs ?",
"position": 12,
"acceptedAnswer": {
"@type": "Answer",
"text": "Mettre en place des protocoles clairs et des revues de cas régulières."
}
},
{
"@type": "Question",
"name": "Comment sensibiliser à l'importance de la collaboration ?",
"position": 13,
"acceptedAnswer": {
"@type": "Answer",
"text": "Organiser des séminaires et des ateliers sur les bénéfices de la collaboration interprofessionnelle."
}
},
{
"@type": "Question",
"name": "Quels outils aident à la prévention des erreurs médicales ?",
"position": 14,
"acceptedAnswer": {
"@type": "Answer",
"text": "Les check-lists et les systèmes de double vérification sont des outils efficaces."
}
},
{
"@type": "Question",
"name": "Comment la prévention impacte-t-elle les soins ?",
"position": 15,
"acceptedAnswer": {
"@type": "Answer",
"text": "Une bonne prévention réduit les erreurs et améliore la satisfaction des patients."
}
},
{
"@type": "Question",
"name": "Comment améliorer les traitements par la collaboration ?",
"position": 16,
"acceptedAnswer": {
"@type": "Answer",
"text": "En intégrant des équipes multidisciplinaires pour une approche globale des soins."
}
},
{
"@type": "Question",
"name": "Quels traitements favorisent la communication ?",
"position": 17,
"acceptedAnswer": {
"@type": "Answer",
"text": "Des formations en communication et des ateliers de team-building sont bénéfiques."
}
},
{
"@type": "Question",
"name": "Comment évaluer l'efficacité des traitements interprofessionnels ?",
"position": 18,
"acceptedAnswer": {
"@type": "Answer",
"text": "Par des études de cas et des analyses de résultats cliniques post-traitement."
}
},
{
"@type": "Question",
"name": "Quels traitements sont adaptés aux équipes interprofessionnelles ?",
"position": 19,
"acceptedAnswer": {
"@type": "Answer",
"text": "Les traitements complexes comme la gestion de la douleur nécessitent une approche collaborative."
}
},
{
"@type": "Question",
"name": "Comment les traitements peuvent-ils être personnalisés ?",
"position": 20,
"acceptedAnswer": {
"@type": "Answer",
"text": "En tenant compte des compétences et des expertises de chaque professionnel de santé."
}
},
{
"@type": "Question",
"name": "Quelles complications résultent d'une mauvaise communication ?",
"position": 21,
"acceptedAnswer": {
"@type": "Answer",
"text": "Des erreurs de traitement et des retards dans les soins peuvent survenir."
}
},
{
"@type": "Question",
"name": "Comment les conflits affectent-ils les résultats des soins ?",
"position": 22,
"acceptedAnswer": {
"@type": "Answer",
"text": "Les conflits peuvent mener à des soins fragmentés et à une insatisfaction des patients."
}
},
{
"@type": "Question",
"name": "Quelles complications peuvent survenir en cas de mauvaise collaboration ?",
"position": 23,
"acceptedAnswer": {
"@type": "Answer",
"text": "Des complications médicales et des réadmissions fréquentes peuvent en résulter."
}
},
{
"@type": "Question",
"name": "Comment évaluer l'impact des complications sur les soins ?",
"position": 24,
"acceptedAnswer": {
"@type": "Answer",
"text": "Par des analyses de données sur les résultats cliniques et la satisfaction des patients."
}
},
{
"@type": "Question",
"name": "Quelles sont les conséquences d'une mauvaise dynamique d'équipe ?",
"position": 25,
"acceptedAnswer": {
"@type": "Answer",
"text": "Une mauvaise dynamique peut entraîner un turnover élevé et une baisse de la qualité des soins."
}
},
{
"@type": "Question",
"name": "Quels facteurs augmentent le risque de conflits ?",
"position": 26,
"acceptedAnswer": {
"@type": "Answer",
"text": "Des différences culturelles et des styles de communication variés peuvent augmenter les conflits."
}
},
{
"@type": "Question",
"name": "Comment le stress influence-t-il les relations interprofessionnelles ?",
"position": 27,
"acceptedAnswer": {
"@type": "Answer",
"text": "Le stress peut réduire la communication et augmenter les tensions entre les professionnels."
}
},
{
"@type": "Question",
"name": "Quels facteurs de risque sont liés à la collaboration ?",
"position": 28,
"acceptedAnswer": {
"@type": "Answer",
"text": "Un manque de formation et des horaires de travail chargés peuvent nuire à la collaboration."
}
},
{
"@type": "Question",
"name": "Comment les différences de statut affectent-elles les relations ?",
"position": 29,
"acceptedAnswer": {
"@type": "Answer",
"text": "Les différences de statut peuvent créer des déséquilibres et des tensions dans l'équipe."
}
},
{
"@type": "Question",
"name": "Quels facteurs environnementaux influencent la collaboration ?",
"position": 30,
"acceptedAnswer": {
"@type": "Answer",
"text": "Un environnement de travail positif et des ressources adéquates favorisent la collaboration."
}
}
]
}
]
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The present study aimed to test the relationship between the components of the Cognitive Load Theory (CLT) including memory, intrinsic and extraneous cognitive load in workplace-based learning in a cl...
This study was conducted at Shahid Sadoughi University of Medical Sciences in 2021-2023. The participants were 151 nursing students who studied their apprenticeship courses in the teaching hospitals. ...
In this study, the goodness of fit of the model based on the cognitive load theory was reported (GIF = 0.99, CFI = 0.99, RMSEA = 0.03). The results of regression analysis showed that the scores of dec...
The present results showed that the CLT in workplace-based learning has a goodness of fit with the components of memory, intrinsic cognitive load, extraneous cognitive load, and clinical decision-maki...
Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating rad...
A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL mod...
Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and interna...
Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with ind...
Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the tri...
This retrospective study was conducted at a tertiary teaching hospital. Data were collected from January 2015 to October 2022. Demographic and clinical information were collected at triage. The study ...
The study analyzed 668,602 ED visits from 2015 to 2022. Among them, 278,724 visits from 2015 to 2018 were used for model training and validation, while 320,201 visits from 2019 to 2022 were for testin...
The traditional 5-level triage system often falls short, leading to under-triage of critical patients. Our models include a score-based differentiation within a triage level to offer advanced risk str...
The assessment of compound blood-brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to ...
Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely interv...
In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable log...
In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, wi...
ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE....
Chictr.org.cn: ChiCTR2200059599....
Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two ...
Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the trans...
We analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichme...
The PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD27...
Our deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immun...
Trauma is one of the most important issues and problems considered in most countries in today's modern and industrial society. Since pre-hospital care is the first component of a trauma care system, i...
The present study was a two-group clinical before/after study in which 96 technicians were selected using a stratified random sampling method. The sample members were randomly divided into an interven...
The results of the repeated measures analysis of variance showed a significant difference between the intervention and control groups in learning skills for dealing with trauma patients (...
The results indicate that training based on the modified team-based learning method is effective for the management of trauma patients by medical emergency personnel and improves the readiness of pers...
Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate...
A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional ne...
Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resecte...
DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to ...
The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appr...
A total of 249 subjects undergoing breast reconstruction surgery were enrolled in this study. Subjects underwent preoperative breast MRI, and the breast region manually outlined by the imaging physici...
The three breast automated segmentation models developed in this study (simple registration model, dynamic programming model, and deep learning model) showed strong ICC with manual segmentation of the...
The three automated breast segmentation algorithms developed in this study generate accurate and reliable breast regions, enable highly reproducible breast region segmentation and automated volume mea...
This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts ...
