Academic literature on the topic 'Family medicine Australia Econometric models'
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Journal articles on the topic "Family medicine Australia Econometric models"
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Zadadaev, Sergey A., and Pavel B. Lukyanov. "SOFT COMPUTING AND SMALL DATA IN AN ECONOMETRIC STUDY OF NATIONAL DEVELOPMENT GOALS." SOFT MEASUREMENTS AND COMPUTING 11, no. 60 (2022): 65–71. http://dx.doi.org/10.36871/2618-9976.2022.11.006.
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The aim of the work was to model the mechanism of the influence of an exogenous resource variable on the predicted indicator of the national development goal, in which one model was selected from an infinite family of hypothetical resource allocation models within a year, reflecting the uncertainty inherent in soft computing. The study of problems based on small sample data in the age of big data is substantiated by the fact that there are a number of longitudinal tasks of longterm research in which, after 5–6 years of testing any methods, it is necessary to obtain data on their effectiveness and predictability in general. At the same time, the data slice occurs strictly at the end of the year as a control and summary measurement. In such cases, special econometric specifications of models are required that take into account the "physics" of relationships. This situation is similar to the situation in mechanics, when small data are compensated by the laws of motion and play the role of initial data in the Cauchy problem for differential equations – when the law itself is known and does not need to be estimated using big data.
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Chege, Patrick Masemiano, J. Penner, P. Godoy-Ruiz, V. Kapoor, J. Rodas, and K. Rouleau. "Evolution of Family Medicine in Kenya (1990s to date): a case study." South African Family Practice 59, no. 2 (March 6, 2017): 46. http://dx.doi.org/10.4102/safp.v59i1.4499.
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Background: Successful Family Medicine practices and academic programmes are found in western countries, Australia, Singapore, Cuba and among other non-western countries. Documenting the enablers and challenges of different contexts would, it is hoped, inform current and future process of developing academic and practice programmes in Family Medicine in countries where the discipline is starting.
Methods: A qualitative study was undertaken that conducted a focused literature review and in-depth interviews of key informants on the early development of the Family Medicine in Kenya. All interviews were audio recorded. Pattern matching, explanation building, time-series analysis and logic models were used in analysis.
Results: Representatives of Kenyan and foreign organisations worked well as a team to write and implement the first curriculum of Family Medicine. The challenges include lack of teachers; starting a graduate programme in medical schools that did not have one and starting these health services delivery departments in a system that did not have any.
Conclusions: The main enablers of the evolution of Family Medicine in Kenya include committed partnerships and teamwork among Kenyan and non-Kenyan stakeholders. The challenges include the lack of Kenyan teachers of the programme and the introduction of a new discipline.
(Full text of the research articles are available online at www.medpharm.tandfonline.com/ojfp)
S Afr Fam Pract 2017; DOI: /10.1080/20786190.2016.12481420
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Mathes, Tim, and Oliver Kuss. "Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics." Research Methods in Medicine & Health Sciences 2, no. 2 (February 17, 2021): 82–89. http://dx.doi.org/10.1177/2632084321996225.
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Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.
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Sherif, Fayroz F., and Khaled S. Ahmed. "Geographic Classification and Identification of SARS-CoV2 From Related Viral Sequences." International Journal of Biology and Biomedical Engineering 15 (July 19, 2021): 254–59. http://dx.doi.org/10.46300/91011.2021.15.31.
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The COVID-19 pandemic has introduced to mild the risks of deadly epidemic-prone illnesses sweeping our globalized planet. The pandemic is still going strong, with additional viral variations popping up all the time. For the close to future, the international response will have to continue. The molecular tests for SARS-CoV-2 detection may lead to False-negative results due to their genetic similarity with other coronaviruses, as well as their ability to mutate and evolve. Furthermore, the clinical features caused by SARS-CoV-2 seem to be like the symptoms of other viral infections, making identification even harder. We constructed seven hidden Markov models for each coronavirus family (SARS-CoV2, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1, MERS-CoV, and SARS-CoV), using their complete genome to accurate diagnose human infections. Besides, this study characterized and classified the SARS-CoV2 strains according to their different geographical regions. We built six SARS-CoV2 classifiers for each world's continent (Africa, Asia, Europe, North America, South America, and Australia). The dataset used was retrieved from the NCBI virus database. The classification accuracy of these models achieves 100% in differentiating any virus model among others in the Coronavirus family. However, the accuracy of the continent models showed a variable range of accuracies, sensitivity, and specificity due to heterogeneous evolutional paths among strains from 27 countries. South America model was the highest accurate model compared to the other geographical models. This finding has vital implications for the management of COVID-19 and the improvement of vaccines.
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Shahunja, K. M., Peter D. Sly, Md Jobayer Chisti, and Abdullah Mamun. "Trajectories of asthma symptom presenting as wheezing and their associations with family environmental factors among children in Australia: evidence from a national birth cohort study." BMJ Open 12, no. 6 (June 2022): e059830. http://dx.doi.org/10.1136/bmjopen-2021-059830.
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ObjectivesAsthma is one of the greatest health burdens, yet contributors to asthma symptom trajectories are understudied in Australian children. We aimed to assess the trajectories of asthma symptom and their associations with several family environmental factors during the childhood period in Australia.DesignSecondary analysis from a cross-sequential cohort study.SettingNationwide representative data from the ‘Longitudinal Study of Australian Children (LSAC)’.ParticipantsParticipants from the LSAC birth cohort.Outcome measuresAsthma symptom trajectory groups.MethodsAsthma symptom presenting as wheezing, family environmental factors and sociodemographic data (2004–2018) were obtained from the LSAC. Group-based trajectory modelling was applied to identify asthma symptom trajectories and multivariable logistic regression models were used to assess the associations between these and environmental factors.ResultsOf 5107 children in the LSAC cohort, 3846 were included in our final analysis. We identified three distinct asthma symptom trajectories from age 0/1 year to 14/15 years: ‘low/no’ (69%), ‘transient high’ (17%) and ‘persistent high’ (14%). Compared with the ‘low/no’ group, children exposed to ‘moderate and declining’ (relative risk ratio (RRR): 2.22, 95% CI 1.94 to 2.54; RRR: 1.26, 95% CI 1.08 to 1.46) and ‘high and persistent’ prevalence of maternal smoking (RRR: 1.41, 95% CI 1.23 to 1.60; RRR: 1.26, 95% CI 1.10 to 1.44) were at increased risk of being classified into the ‘transient high’ and ‘persistent high’ trajectories of asthma symptom. Persistently bad external dwelling conditions (RRR: 1.27, 95% CI 1.07 to 1.51) were associated with ‘transient high’ trajectory while ‘moderate and increasing’ conditions of cluttered homes (RRR: 1.37, 95% CI 1.20 to 1.56) were associated with ‘persistent high’ trajectory of asthma symptom. Exposure to tobacco smoke inside the house also increased the risk of being in the ‘persistent high’ trajectory group (RRR: 1.30, 95% CI 1.12 to 1.50).ConclusionPoor home environment increased the risk of asthma symptom during childhood. Improving home environment and reducing exposure to tobacco smoke may facilitate a favourable asthma symptom trajectory during childhood.
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Shahunja, K. M., Peter D. Sly, Md Jobayer Chisti, and Abdullah Mamun. "Trajectories of asthma symptom presenting as wheezing and their associations with family environmental factors among children in Australia: evidence from a national birth cohort study." BMJ Open 12, no. 6 (June 2022): e059830. http://dx.doi.org/10.1136/bmjopen-2021-059830.
Full textAbstract:
ObjectivesAsthma is one of the greatest health burdens, yet contributors to asthma symptom trajectories are understudied in Australian children. We aimed to assess the trajectories of asthma symptom and their associations with several family environmental factors during the childhood period in Australia.DesignSecondary analysis from a cross-sequential cohort study.SettingNationwide representative data from the ‘Longitudinal Study of Australian Children (LSAC)’.ParticipantsParticipants from the LSAC birth cohort.Outcome measuresAsthma symptom trajectory groups.MethodsAsthma symptom presenting as wheezing, family environmental factors and sociodemographic data (2004–2018) were obtained from the LSAC. Group-based trajectory modelling was applied to identify asthma symptom trajectories and multivariable logistic regression models were used to assess the associations between these and environmental factors.ResultsOf 5107 children in the LSAC cohort, 3846 were included in our final analysis. We identified three distinct asthma symptom trajectories from age 0/1 year to 14/15 years: ‘low/no’ (69%), ‘transient high’ (17%) and ‘persistent high’ (14%). Compared with the ‘low/no’ group, children exposed to ‘moderate and declining’ (relative risk ratio (RRR): 2.22, 95% CI 1.94 to 2.54; RRR: 1.26, 95% CI 1.08 to 1.46) and ‘high and persistent’ prevalence of maternal smoking (RRR: 1.41, 95% CI 1.23 to 1.60; RRR: 1.26, 95% CI 1.10 to 1.44) were at increased risk of being classified into the ‘transient high’ and ‘persistent high’ trajectories of asthma symptom. Persistently bad external dwelling conditions (RRR: 1.27, 95% CI 1.07 to 1.51) were associated with ‘transient high’ trajectory while ‘moderate and increasing’ conditions of cluttered homes (RRR: 1.37, 95% CI 1.20 to 1.56) were associated with ‘persistent high’ trajectory of asthma symptom. Exposure to tobacco smoke inside the house also increased the risk of being in the ‘persistent high’ trajectory group (RRR: 1.30, 95% CI 1.12 to 1.50).ConclusionPoor home environment increased the risk of asthma symptom during childhood. Improving home environment and reducing exposure to tobacco smoke may facilitate a favourable asthma symptom trajectory during childhood.
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Nisselle, Amy, Emily A. King, Belinda McClaren, Monika Janinski, Sylvia Metcalfe, and Clara Gaff. "Measuring physician practice, preparedness and preferences for genomic medicine: a national survey." BMJ Open 11, no. 7 (July 2021): e044408. http://dx.doi.org/10.1136/bmjopen-2020-044408.
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ObjectiveEven as genomic medicine is implemented globally, there remains a lack of rigorous, national assessments of physicians’ current genomic practice and continuing genomics education needs. The aim of this study was to address this gap.DesignA cross-sectional survey, informed by qualitative data and behaviour change theory, to assess the current landscape of Australian physicians’ genomic medicine practice, perceptions of proximity and individual preparedness, and preferred models of practice and continuing education. The survey was advertised nationally through 10 medical colleges, 24 societies, 62 hospitals, social media, professional networks and snowballing.Results409 medical specialists across Australia responded, representing 30 specialties (majority paediatricians, 20%), from mainly public hospitals (70%) in metropolitan areas (75%). Half (53%) had contacted their local genetics services and half (54%) had ordered or referred for a gene panel or exome/genome sequencing test in the last year. Two-thirds (67%) think genomics will soon impact their practice, with a significant preference for models that involved genetics services (p<0.0001). Currently, respondents mainly perform tasks associated with pretest family history taking and counselling, but more respondents expect to perform tasks at all stages of testing in the future, including tasks related to the test itself, and reporting results. While one-third (34%) recently completed education in genomics, only a quarter (25%) felt prepared to practise. Specialists would like (more) education, particularly on genomic technologies and clinical utility, and prefer this to be through varied educational strategies.ConclusionsThis survey provides data from a breadth of physician specialties that can inform models of genetic service delivery and genomics education. The findings support education providers designing and delivering education that best meet learner needs to build a competent, genomic-literate workforce. Further analyses are underway to characterise early adopters of genomic medicine to inform strategies to increase engagement.
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Trussell, Dawn E. "Building Inclusive Communities in Youth Sport for Lesbian-Parented Families." Journal of Sport Management 34, no. 4 (July 1, 2020): 367–77. http://dx.doi.org/10.1123/jsm.2019-0395.
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This interpretative study examines the complexities of lesbian parents’ experiences in organized youth sport programs. Specifically, it seeks to understand youth sport as a potential site for social change that facilitates a sense of inclusive community for diverse family structures. Using thematic analysis, the author examines perspectives of nine participants from Australia, Canada, and the United States. Emphasis is placed on how the lesbian parents (a) negotiate heightened visibility, sexual stigma, and parental judgment; (b) foster social relationships through participation, volunteerism, and positive role models; and (c) create shared understanding toward building an inclusive sport culture. The findings call attention to the importance of intentional and unintentional acts (by families as well as sport organizations) that create a sense of community and an inclusive organizational culture. The connection of lesbian parents’ experiences to broader concepts, such as sexual stigma and transformative services, are also examined within the context of youth sport.
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O'Connor, Margaret, Louise Peters, Susan Lee, and Cecilia Webster. "Palliative Care Work, Between Death and Discharge." Journal of Palliative Care 21, no. 2 (June 2005): 97–102. http://dx.doi.org/10.1177/082585970502100205.
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This paper discusses a small study that was undertaken in a palliative care unit (PCU) to demonstrate reasons for the lengthy patient turnover time after the death of a patient. The study arose from a managerial challenge about the PCU's efficiency in comparison to other settings of care. While Palliative care services in Australia are loosely funded on a per diem rate, casemix funding models for inpatient services in other settings reward efficiency in terms of length of stay (1). The aim of the study was to undertake a post-death survey, from the nursing perspective, of activities and the time required to deliver follow-up care, as required, to the deceased patient and his/her family. Details of the post-death episode for all patients who died in the PCU over two time periods were collected using a newly designed tool. This paper does not argue for the incorporation of post-death nursing work as a measure of efficiency or quality; rather, it is a demonstration of the type of work and the time involved in carrying out post-death work. This nursing work is an essential but hidden and generally unacknowledged element of palliative care and, not surprisingly, there is a dearth of literature describing the role of the nurse in providing care of the family and others after the patient has died. This project has provided important information about the factors that Protract after-death care, and gives some insight into the role of the nurse after a death occurs in a PCU.
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Seehusen, Dean A., Meghan F. Raleigh, Julie P. Phillips, Jacob Prunuske, Christopher P. Morley, Molly E. Polverento, Iris Kovar-Gough, and Andrea L. Wendling. "Institutional Characteristics Influencing Medical Student Selection of Primary Care Careers: A Narrative Review and Synthesis." Family Medicine 54, no. 7 (July 5, 2022): 522–30. http://dx.doi.org/10.22454/fammed.2022.837424.
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Background and Objectives: There is an ongoing shortage of primary care physicians in the United States. Medical schools are under pressure to address this threat to the nation’s health by producing more primary care graduates, including family physicians. Our objective was to identify institutional characteristics associated with more medical students choosing primary care. Methods: We conducted a systematic literature review with narrative synthesis to identify medical school characteristics associated with increased numbers or proportions of primary care graduates. We included peer-reviewed, published research from the United States, Canada, Australia, and New Zealand. The existing literature on characteristics, including institutional geography, funding and governance, mission, and research emphasis, was analyzed and synthesized into summary statements. Results: Ensuring a strong standing of the specialty of family medicine and creating an atmosphere of acceptance of the pursuit of primary care as a career are likely to increase an institution’s percentage of medical students entering primary care. Training on regional campuses or providing primary care experiences in rural settings also correlates with a larger percentage of graduates entering primary care. A research-intensive culture is inversely correlated with primary care physician production among private, but not public, institutions. The literature on institutional financial incentives is not of high enough quality to make a firm statement about influence on specialty choice. Conclusions: To produce more primary care providers, medical schools must create an environment where primary care is supported as a career choice. Medical schools should also consider educational models that incorporate regional campuses or rural educational settings.