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Action Research to Improve the Learning Space for Diagnostic Techniques.

ABSTRACT: The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of "knowledge" and "understanding." The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage in deep learning using a variety of learning styles. The effectiveness of the new curriculum was assessed by means of on-course student assessment throughout the module, a final exam, an anonymous questionnaire on student evaluation of the different activities and a focus group of volunteers. Although the new curriculum enabled a major part of the student cohort to achieve higher pass grades (p < 0.001), it did not meet the requirements of the weaker students, and the proportion of the students failing the module remained at 34%. The action research applied here provided a number of valuable suggestions from students on how to improve future curricula from their perspective. Most importantly, an interactive online program that facilitated flexibility in the learning space for the different reagents and their interaction in diagnostic tests was proposed. The methods applied to improve and assess a curriculum refresh by involving students as partners in the process, as well as the outcomes, are discussed. Journal of Microbiology & Biology Education.

SUBMITTER: Ariel E

PROVIDER: S-EPMC4690557 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Action Research to Improve the Learning Space for Diagnostic Techniques.

Ariel Ellen E Owens Leigh L

Journal of microbiology & biology education 20151201 2

The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of "knowledge" and "understanding." The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage i ...[more]

PMID: 26753024

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Project description:BackgroundWhile integral to decentralising health reforms, Community Health Workers (CHWs) in South Africa experience many challenges. During COVID-19, CHW roles changed rapidly, shifting from communities to clinics. In the contexts of new roles and re-engineered primary healthcare (PHC), the objectives were to: (a) implement a training intervention to support local decision-making capability of CHWs; and (b) assess learning and impacts from the perspectives of CHWs.MethodsCHWs from three rural villages (n = 9) were trained in rapid Participatory Action Research (PAR) with peers and community stakeholders (n = 33). Training equipped CHWs with tools and techniques to convene community groups, raise and/or respond to local health concerns, understand concerns from different perspectives, and facilitate action in communities and public services. CHWs' perspectives before and after the intervention were gained through semi-structured interviews. Data were collected and analysed using the decision space framework to understand local actors' power to affect devolved decision-making.ResultsCHWs demonstrated significant resilience and commitment in the face of COVID-19. They experienced multiple, intersecting challenges including: limited financial, logistical and health systems support, poor role clarity, precarious employment, low and no pay, unstable organisational capacity, fragile accountability mechanisms and belittling treatment in clinics. Together, these restricted decision space and were seen to reflect a low valuing of the cadre in the system. CHWs saw the training as a welcome opportunity to assert themselves as a recognised cadre. Regular, spaces for dialogue and mutual learning supported CHWs to gain tools and skills to rework their agency in more empowered ways. The training improved management capacity, capabilities for dialogue, which expanded role clarity, and strengthened community mobilisation, facilitation and analysis skills. Development of public speaking skills was especially valued. CHWs reported an overall 'tripe-benefit' from the training: community-acceptance; peer support; and dialogue with and recognition by the system. The training intervention was recommended for scale-up by the health authority as an implementation support strategy for PHC.ConclusionsLack of recognition of CHWs is coupled with limited opportunities for communication and trust-building. The training supported CHWs to find and amplify their voices in strategic partnerships, and helped build functionality for local decision-making.

Project description:IntroductionThe single biggest barrier for countries in sub-Saharan Africa (SSA) to scale up the necessary health services for addressing the three health-related Millennium Development Goals and achieving Universal Health Coverage is the lack of an adequate and well-performing health workforce. This deficit needs to be addressed both by training more new health personnel and by improving the performance of the existing and future health workforce. However, efforts have mostly been focused on training new staff and less on improving the performance of the existing health workforce. The purpose of this paper is to disseminate the protocol for the PERFORM project and reflect on the key challenges encountered during the development of this methodology and how they are being overcome.MethodsThe overall aim of the PERFORM project is to identify ways of strengthening district management in order to address health workforce inadequacies by improving health workforce performance in SSA. The study will take place in three districts each in Ghana, Tanzania and Uganda using an action research approach. With the support of the country research teams, the district health management teams (DHMTs) will lead on planning, implementation, observation, reflection and redefinition of the activities in the study. Taking into account the national and local human resource (HR) and health systems (HS) policies and practices already in place, 'bundles' of HR/HS strategies that are feasible within the context and affordable within the districts' budget will be developed by the DHMTs to strengthen priority areas of health workforce performance. A comparative analysis of the findings from the three districts in each country will add new knowledge on the effects of these HR/HS bundles on DHMT management and workforce performance and the impact of an action research approach on improving the effectiveness of the DHMTs in implementing these interventions.DiscussionDifferent challenges were faced during the development of the methodology. These include the changing context in the study districts, competing with other projects and duties for the time of district managers, complexity of the study design, maintaining the anonymity and confidentiality of study participants as well as how to record the processes during the study. We also discuss how these challenges are being addressed. The dissemination of this research protocol is intended to generate interest in the PERFORM project and also stimulate discussion on the use of action research in complex studies such as this on strengthening district health management to improve health workforce performance.

Project description:BackgroundDental plaque microbes play a key role in the development of periodontal disease. Numerous high-throughput sequencing studies have generated understanding of the bacterial species associated with both canine periodontal health and disease. Opportunities therefore exist to utilise these bacterial biomarkers to improve disease diagnosis in conscious-based veterinary oral health checks. Here, we demonstrate that molecular techniques, specifically quantitative polymerase chain reaction (qPCR) can be utilised for the detection of microbial biomarkers associated with canine periodontal health and disease.ResultsOver 40 qPCR assays targeting single microbial species associated with canine periodontal health, gingivitis and early periodontitis were developed and validated. These were used to quantify levels of the respective taxa in canine subgingival plaque samples collected across periodontal health (PD0), gingivitis (PD1) and early periodontitis (PD2). When qPCR outputs were compared to the corresponding high-throughput sequencing data there were strong correlations, including a periodontal health associated taxa, Capnocytophaga sp. COT-339 (rs =0.805), and two periodontal disease associated taxa, Peptostreptococcaceae XI [G-4] sp. COT-019 (rs=0.902) and Clostridiales sp. COT-028 (rs=0.802). The best performing models, from five machine learning approaches applied to the qPCR data for these taxa, estimated 85.7% sensitivity and 27.5% specificity for Capnocytophaga sp. COT-339, 74.3% sensitivity and 67.5% specificity for Peptostreptococcaceae XI [G-4] sp. COT-019, and 60.0% sensitivity and 80.0% specificity for Clostridiales sp. COT-028.ConclusionsA qPCR-based approach is an accurate, sensitive, and cost-effective method for detection of microbial biomarkers associated with periodontal health and disease. Taken together, the correlation between qPCR and high-throughput sequencing outputs, and early accuracy insights, indicate the strategy offers a prospective route to the development of diagnostic tools for canine periodontal disease.

Project description:BackgroundMachine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation.MethodA systematic search was conducted in five electronic databases from January 2000 to June 2022. Then, the title, abstracts, and full text of extracted articles were screened based on the PRISMA checklist. Then, eligible articles were selected according to inclusion criteria. The information regarding developed models was extracted from reviewed articles using a data extraction sheet.ResultsSearches yielded 414 citations. Of them, 136 studies were excluded after the title and abstract screening. Finally, 16 articles were determined as eligible studies that met our inclusion criteria. The objectives of eligible articles are classified into eight main categories. The applied machine learning methods include the Support vector machine (SVM) (n = 5, 31.25%) technique, logistic regression (n = 4, 25%), Random Forests (RF) (n = 4, 25%), Bayesian network (BN) (n = 3, 18.75%), linear regression (LR) (n = 3, 18.75%), Decision Tree (DT) (n = 3, 18.75%), neural networks (n = 3, 18.75%), Markov Model (n = 1, 6.25%), KNN (n = 1, 6.25%), K-means (n = 1, 6.25%), Gradient Boosting trees (XGBoost) (n = 1, 6.25%), and Convolutional Neural Network (CNN) (n = 1, 6.25%). Most studies (n = 11) employed more than one machine learning technique or combination of different techniques to make their models. The data obtained from pulmonary function tests were the most used as input variables in predictive model development. Most studies (n = 10) used only post-transplant patient information to develop their models. Also, UNOS was recognized as the most desirable data source in the reviewed articles. In most cases, clinicians succeeded to predict acute diseases incidence after lung transplantation (n = 4) or estimate survival rate (n = 4) by developing machine learning models.ConclusionThe outcomes of these developed prediction models could aid clinicians to make better and more reliable decisions by extracting new knowledge from the huge volume of lung transplantation data.

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Action Research to Improve the Learning Space for Diagnostic Techniques.

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Action Research to Improve the Learning Space for Diagnostic Techniques.

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