Knowledge and use of asthma control measurement tools in the management of asthma: a survey of doctors working in family and internal medicine practice in Nigeria.

Objective: To investigate the knowledge and use of asthma control measurement (ACM) tools in the management of asthma among doctors working in family and internal medicine practice in Nigeria. Method: A questionnaire based on the global initiative on asthma (GINA) guideline was self-administered by 194 doctors. It contains 12 test items on knowledge of ACM tools and its application. The knowledge score was obtained by adding the correct answers and classified as good if the score ≥ 9, satisfactory if score was 6-8 and poor if < 6. Results: The overall doctors knowledge score of ACM tools was 4.49±2.14 (maximum of 12). Pulmonologists recorded the highest knowledge score of 10.75±1.85. The majority (69.6%) had poor knowledge score of ACM tools. Fifty (25.8%) assessed their patients’ level of asthma control and 34(17.5%) at every visit. Thirty-nine (20.1%) used ACM tools in their consultation, 29 (15.0%) of them used GINA defined control while 10 (5.2 %) used asthma control test (ACT). The use of the tools was associated with pulmonologists, having attended CME within six months and graduated within five years prior to the survey. Conclusion: The results highlight the poor knowledge and use of ACM tools and the need to address the knowledge gap.

Inter-professional education unveiling significant association between asthma knowledge and inhaler technique

Objectives: To explore whether an association exists between health care professionals’ (HCPs) asthma knowledge and inhaler technique demonstration skills. Methods: HCPs’ asthma knowledge and inhaler technique demonstration skills were assessed at baseline at an inter-professional educational workshop focusing on asthma medication use. Asthma knowledge was assessed via a published questionnaire. Correct inhaler technique for the three inhalers, the Accuhaler, Turbuhaler and pressurized Metered Dose Inhaler (pMDI) was assessed using published checklists. Results: Two hundred HCPs agreed to participate: 10 specialists (medical doctors specialized in respiratory diseases) (5%), 46 general practitioners (23%), 79 pharmacists (39%), 15 pharmacists’ assistants (8%), 40 nurses (20%) and 10 respiratory therapists (5%). Backwards stepwise multiple regression conducted to determine predictors of HCPs’ inhaler technique, showed that out of many independent variables (asthma knowledge score, profession, age, gender, place of work, years in practice and previous personal use of the study inhaler/s), asthma knowledge score was the only variable showing significant association with inhaler technique (R²=0.162, p<0.001). Conclusion: This study revealed significant associations between asthma knowledge and inhaler technique scores for all HCPs. Providing inter-professional workshops for all HCPs involved integrating education on asthma knowledge and practice of inhaler technique skills are looked-for.

Access, attitudes and training in information technologies and evidence-based medicine among medical students at University of Zimbabwe College of Health Sciences.

Background: The Medical Education Partnership Initiative, has helped to mitigate the digital divide in Africa. The aim of the study was to assess the level of access, attitude, and training concerning meaningful use of electronic resources and EBM among medical students at an African medical school. Methods: The study involved medical students at the University of Zimbabwe College of Health Sciences, Harare. The needs assessment tool consisted of a 21-question, paper-based, voluntary and anonymous survey. Results: A total of 61/67 (91%), responded to the survey. 60% of the medical students were ‘third-year medical students’. Among medical students, 85% of responders had access to digital medical resources, but 54% still preferred printed medical textbooks. Although 25% of responders had received training in EBM, but only 7% found it adequate. 98% of the participants did not receive formal training in journal club presentation or analytical reading of medical literature, but 77 % of them showed interest in learning these skills. Conclusion: Lack of training in EBM, journal club presentation and analytical reading skills have limited the impact of upgraded technology in enhancing the level of knowledge. This impact can be boosted by developing a curriculum with skills necessary in using EBM.

Knowledge and perceptions of quality of obstetric and newborn care of local health providers: a cross-sectional study in three districts in Malawi

Aim Quality of service delivery for maternal and newborn health in Malawi is influenced by human resource shortages and knowledge and care practices of the existing service providers. We assessed Malawian healthcare providers’ knowledge of management of routine labour, emergency obstetric care and emergency newborn care; correlated knowledge with reported confidence and previous study or training; and measured perception of the care they provided. Methods his study formed part of a large-scale quality of care assessment in three districts (Kasungu, Lilongwe and Salima) of Malawi. Subjects were selected purposively by their role as providers of obstetric and newborn care during routine visits to health facilities by a research assistant. Research assistants introduced and supervised the self-completed questionnaire by the service providers. Respondents included 42 nurse midwives, 1 clinical officer, 4 medical assistants and 5 other staff. Of these, 37 were staff working in facilities providing Basic Emergency Obstetric Care (BEMoC) and 15 were from staff working in facilities providing Comprehensive Emergency Obstetric Care (CEMoC). Results Knowledge regarding management of routine labour was good (80% correct responses), but knowledge of correct monitoring during routine labour (35% correct) was not in keeping with internationally recognized good practice. Questions regarding emergency obstetric care were answered correctly by 70% of respondents with significant variation depending on clinicians’ place of work. Knowledge of emergency newborn care was poor across all groups surveyed with 58% correct responses and high rates of potentially life-threatening responses from BEmOC facilities. Reported confidence and training had little impact on levels of knowledge. Staff in general reported perception of poor quality of care. Conclusion Serious deficiencies in providers’ knowledge regarding monitoring during routine labour and management of emergency newborn care were documented. These may contribute to maternal and neonatal deaths in Malawi. The knowledge gap cannot be overcome by simply providing more training.

Knowledge about the rncci: an exploratory study about the perception of nursing students

The National Network of Continuing Care (RNCCI) was created in 2006 by Decree Law nr. 101/2006. Its mission is to supply adequate health and social care to all people who, independent of their age, are in a situation of dependence, and its action is articulated with the already existing health and social services, being a multidisciplinary team needed composed out of medical doctors, nurses, social workers and psychologists. Given the aforementioned it’s pertinent to perform research, with nurses and nursing students, about this new valence of care.

Evaluation of Educational Environment for Medical Students of a Tertiary Pediatric Hospital in Tehran, Using DREEM Questionnaire

Background: Tertiary pediatric hospitals usually provide excellent clinical services, but such centers have a lot to do for educational perfection. Objectives: This study was performed to address under-graduate educational deficits and find feasible solutions. Patients and Methods: This cross-sectional study was done in a target population of 77 sixth year undergraduate medical students (response rate = 78%) who spent their 3-month pediatric rotation in the Children’s Medical Center, the Pediatrics Center of Excellence in Tehran, Iran. The Dundee ready educational environment measure (DREEM) instrument was used for assessing educational environment of this subspecialized pediatric hospital. Results: Among 60 students who answered the questionnaires, 24 were male (40%). Participants’ age ranged from 23 to 24 years. The mean total score was 95.8 (48%). Comparison of scores based on students’ knowledge showed no significant difference. Problematic areas were learning, academic self-perception, and social self-perception. Conclusions: Having an accurate schedule to train general practitioner, using new teaching methods, and providing a non-stressful atmosphere were suggested solutions.

Waiting Time Screening in Diagnostic Medical Imaging - A Case-Based View

Due to the high standards expected from diagnostic medical imaging, the analysis of information regarding waiting lists via different information systems is of utmost importance. Such analysis, on the one hand, may improve the diagnostic quality and, on the other hand, may lead to the reduction of waiting times, with the concomitant increase of the quality of services and the reduction of the inherent financial costs. Hence, the purpose of this study is to assess the waiting time in the delivery of diagnostic medical imaging services, like computed tomography and magnetic resonance imaging. Thereby, this work is focused on the development of a decision support system to assess waiting times in diagnostic medical imaging with recourse to operational data of selected attributes extracted from distinct information systems. The computational framework is built on top of a Logic Programming Case-base Reasoning approach to Knowledge Representation and Reasoning that caters for the handling of in-complete, unknown, or even self-contradictory information.

Nanostructured smart materials as functional components of medical devices

The field of medical devices has experienced, more than others, technological advances, developments and innovations, thanks to the rapidly expanding scientific knowledge and collaboration between different disciplines such as biology, engineering and materials science. The design of functional components can be achieved by exploiting composite materials based on nanostructured smart materials, that due to the inherent characteristics of single constituents develop unique properties that make them suitable for different applications preserving excellent mechanical proprieties. For instance, recent developments have focused on the fabrication of piezoelectric devices with multiple biomedical functions, as actuation and sensing functions in one component for monitoring pressure signals. The present Ph.D. Thesis aims at investigating nanostructured smart materials embedded into a polymeric matrix to obtain a composite material that can be used as a functional component for medical devices. (i) Nanostructured piezoelectric material with self-sensing capability was successfully manufactured by using ceramic (i.e. lead zirconate titanate (PZT)) and (ii) polymeric (i.e. poly(vinylidene fluoride-trifluoro ethylene (PVDF-TRFE)) piezoelectric materials. PZT nanofibers were obtained by sol-gel electrospinning starting from synthetized PZT precursor solution. Synthesis, sol-gel electrospinning process, and thermal treatment were accurately controlled to obtain PZT nanofibers dimensionally stable with densely packed grains in the perovskite phase. To guarantee the impact resistance of the laminate, the morphology and size of the hosting filler were accurately designed by increasing the surface area to volume ratio. Moreover, to solve the issue relative to the mechanical discrepancy between rigid electronic materials/soft human tissues/different material of the device (iii) a nanostructured flexible composite material based on a network of Poly-L-lactic acid (PLLA) made of curled nanofibers that present a tuneable mechanical response as a function of the applied stress was successful fabricated.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.

Vocabulary Transfer and Knowledge Distillation for Language Model Compression

Natural Language Processing (NLP) has seen tremendous improvements over the last few years. Transformer architectures achieved impressive results in almost any NLP task, such as Text Classification, Machine Translation, and Language Generation. As time went by, transformers continued to improve thanks to larger corpora and bigger networks, reaching hundreds of billions of parameters. Training and deploying such large models has become prohibitively expensive, such that only big high tech companies can afford to train those models. Therefore, a lot of research has been dedicated to reducing a model’s size. In this thesis, we investigate the effects of Vocabulary Transfer and Knowledge Distillation for compressing large Language Models. The goal is to combine these two methodologies to further compress models without significant loss of performance. In particular, we designed different combination strategies and conducted a series of experiments on different vertical domains (medical, legal, news) and downstream tasks (Text Classification and Named Entity Recognition). Four different methods involving Vocabulary Transfer (VIPI) with and without a Masked Language Modelling (MLM) step and with and without Knowledge Distillation are compared against a baseline that assigns random vectors to new elements of the vocabulary. Results indicate that VIPI effectively transfers information of the original vocabulary and that MLM is beneficial. It is also noted that both vocabulary transfer and knowledge distillation are orthogonal to one another and may be applied jointly. The application of knowledge distillation first before subsequently applying vocabulary transfer is recommended. Finally, model performance due to vocabulary transfer does not always show a consistent trend as the vocabulary size is reduced. Hence, the choice of vocabulary size should be empirically selected by evaluation on the downstream task similar to hyperparameter tuning.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

Delays In Receiving Obstetric Care And Poor Maternal Outcomes: Results From A National Multicentre Cross-sectional Study.

The vast majority of maternal deaths in low-and middle-income countries are preventable. Delay in obtaining access to appropriate health care is a fairly common problem which can be improved. The objective of this study was to explore the association between delay in providing obstetric health care and severe maternal morbidity/death. This was a multicentre cross-sectional study, involving 27 referral obstetric facilities in all Brazilian regions between 2009 and 2010. All women admitted to the hospital with a pregnancy-related cause were screened, searching for potentially life-threatening conditions (PLTC), maternal death (MD) and maternal near-miss (MNM) cases, according to the WHO criteria. Data on delays were collected by medical chart review and interview with the medical staff. The prevalence of the three different types of delays was estimated according to the level of care and outcome of the complication. For factors associated with any delay, the PR and 95%CI controlled for cluster design were estimated. A total of 82,144 live births were screened, with 9,555 PLTC, MNM or MD cases prospectively identified. Overall, any type of delay was observed in 53.8% of cases; delay related to user factors was observed in 10.2%, 34.6% of delays were related to health service accessibility and 25.7% were related to quality of medical care. The occurrence of any delay was associated with increasing severity of maternal outcome: 52% in PLTC, 68.4% in MNM and 84.1% in MD. Although this was not a population-based study and the results could not be generalized, there was a very clear and significant association between frequency of delay and severity of outcome, suggesting that timely and proper management are related to survival.

A Brazilian Original Pedagogical Approach To The Teaching Of Neurology.

The two-arm Clinical Decisions/Diagnostic Workshop (CD/DW) approach to undergraduate medical education has been successfully used in Brazil. Present the CD/DW approach to the teaching of stroke, with the results of its pre-experimental application and of a comparative study with the traditional lecture-case discussion approach. Application of two questionnaires (opinion and Knowledge-Attitudes-Perceptions-KAP) to investigate the non-inferiority of the CD/DW approach. The method was well accepted by teachers and students alike, the main drawback being the necessarily long time for its completion by the students, a feature that may better cater for different educational needs. The comparative test showed the CD/DW approach to lead to slightly higher cognitive acquisition as opposed to the traditional method, clearly showing its non-inferiority status. The CD/DW approach seems to be another option for teaching neurology in undergraduate medical education, with the bonus of respecting each learner`s time.

[between The Country And The Laboratory: The Dynamics Of The Production Of Knowledge At The Menopause Outpatients Clinic At Caism, Unicamp.]

This study investigates the practices involved in the production of knowledge about menopause at Caism, Unicamp, a reference center for public policies for women's health. Gynecological appointments and psychological support meetings were observed, and women and doctors were interviewed in order to identify what discourse circulates there and how different actors are brought in to ensure that the knowledge produced attains credibility and travels beyond the boundaries of the teaching hospital to become universal. The analysis is based on localized studies aligned with social studies of science and technology.

[between The Country And The Laboratory: The Dynamics Of The Production Of Knowledge At The Menopause Outpatients Clinic At Caism, Unicamp].

This study investigates the practices involved in the production of knowledge about menopause at Caism, Unicamp, a reference center for public policies for women's health. Gynecological appointments and psychological support meetings were observed, and women and doctors were interviewed in order to identify what discourse circulates there and how different actors are brought in to ensure that the knowledge produced attains credibility and travels beyond the boundaries of the teaching hospital to become universal. The analysis is based on localized studies aligned with social studies of science and technology.