Sexual reproduction and population genetics of the clonal dioecious macrophyte Vallisneria americana Michx.

Populations of clonal species are predicted to be genetically depauperate compared to non-clonal species, as clones growth tends to be the primary mode of reproduction in these populations. Vallisneria americana is a dioecious, clones aquatic macrophyte that regularly flowers and produces seed. Sites from the Great Lakes (20) and Florida (5) were assayed for allozyme polymorphisms. The sites were found to be genetically variable (mean H exp = 0.216), and multiclonal (D range 0.744--0.967). Little genetic differentiation was detected among Great Lakes sites (FST = 0.038); Florida sites were somewhat differentiated (FST = 0.122) corresponding to geographical disjunction, and were significantly different from Great Lakes sites (FST = 0.124). This lack of differentiation among sites within a water system, coupled with high levels of genetic variation and clones diversity, suggest that sexual reproduction and gene flow are significant evolutionary processes in V. americana populations. Studies of sexual reproduction in V. americana showed that male and female shoots did not differentially compete with each other, nor were they allelopathic. Nutrient regimes, sediment composition and light differences were not correlated with flowering, seed production and biomass at three sites in the Huron-Erie corridor. In controlled pollinations, outcrossing and inbreeding effects were not detected in seed production or seedling vigour measures in crosses between nearby mates nor between distant mates. Differences in seedling performance were, however, associated with maternal identity. Vallisneria americana populations in the Huron-Erie corridor were found to produce significant numbers of seeds, and maintain a seed bank. Despite an efficient mode of clonal reproduction, sexual reproduction appears to play a significant role in populations of V. americana , a finding similar to that for a range of clonal plant and animal species. This role, and the potential for significant gene flow among sites are considered in the context of population genetics and evolutionary processes in populations of V. americana.Dept. of Biological Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .L65. Source: Dissertation Abstracts International, Volume: 61-09, Section: B, page: 4530. Adviser: Lesley Lovett-Doust. Thesis (Ph.D.)--University of Windsor (Canada), 2000

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented

Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review

Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. A systematic search of MEDLINE, EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies published between January 2010 and February 2022. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed, and the results were narratively described. Of the 2011 non-duplicated citations, 8 journal articles and 2 conference proceedings met inclusion criteria. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations as ground truth. The hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI, 1.2-2.6) and 1.8 (95% CI, 1.3-2.7) to predict distant disease-free survival; 1.91 (95% CI, 1.11-3.29) for recurrence, and between 0.09 (95% CI, 0.01-0.70) and 0.65 (95% CI, 0.43-0.98) for overall survival, using clinical data as ground truth. Despite EV being an important step before the clinical application of a ML model, it hasn't been performed routinely. The large variability in the training/validation datasets, methods, performance metrics, and reported information limited the comparison of the models and the analysis of their results (...

The relationship between patient experience and real-world digital health access in primary care: A population-based cross-sectional study.

Implementing digital health technologies in primary care is anticipated to improve patient experience. We examined the relationships between patient experience and digital health access in primary care settings in Ontario, Canada. We conducted a retrospective cross-sectional study using patient responses to the Health Care Experience Survey linked to health and administrative data between April 2019-February 2020. We measured patient experience by summarizing HCES questions. We used multivariable logistic regression stratified by the number of primary care visits to investigate associations between patient experience with digital health access and moderating variables. Our cohort included 2,692 Ontario adults, of which 63.0% accessed telehealth, 2.6% viewed medical records online, and 3.6% booked appointments online. Although patients reported overwhelmingly positive experiences, we found no consistent relationship with digital health access. Online appointment booking access was associated with lower odds of poor experience for patients with three or more primary care visits in the past 12 months (adjusted odds ratio 0.16, 95% CI 0.02-0.56). Younger age, tight financial circumstances, English as a second language, and knowing their primary care provider for fewer years had greater odds of poor patient experience. In 2019/2020, we found limited uptake of digital health in primary care and no clear association between real-world digital health adoption and patient experience in Ontario. Our findings provide an essential context for ensuing rapid shifts in digital health adoption during the COVID-19 pandemic, serving as a baseline to reexamine subsequent improvements in patient experience

A cross-sectional study of the number and frequency of terms used to refer to knowledge translation in a body of health literature in 2006: a Tower of Babel?

<p/> <p>Background</p> <p>The study of implementing research findings into practice is rapidly growing and has acquired many competing names (<it>e.g</it>., dissemination, uptake, utilization, translation) and contributing disciplines. The use of multiple terms across disciplines pose barriers to communication and progress for applying research findings. We sought to establish an inventory of terms describing this field and how often authors use them in a collection of health literature published in 2006.</p> <p>Methods</p> <p>We refer to this field as knowledge translation (KT). Terms describing aspects of KT and their definitions were collected from literature, the internet, reports, textbooks, and contact with experts. We compiled a database of KT and other articles by reading 12 healthcare journals representing multiple disciplines. All articles published in these journals in 2006 were categorized as being KT or not. The KT articles (all KT) were further categorized, if possible, for whether they described KT projects or implementations (KT application articles), or presented the theoretical basis, models, tools, methods, or techniques of KT (KT theory articles). Accuracy was checked using duplicate reading. Custom designed software determined how often KT terms were used in the titles and abstracts of articles categorized as being KT.</p> <p>Results</p> <p>A total of 2,603 articles were assessed, and 581 were identified as KT articles. Of these, 201 described KT applications, and 153 included KT theory. Of the 100 KT terms collected, 46 were used by the authors in the titles or abstracts of articles categorized as being KT. For all 581 KT articles, eight terms or term variations used by authors were highly discriminating for separating KT and non-KT articles (p < 0.001): implementation, adoption, quality improvement, dissemination, complex intervention (with multiple endings), implementation (within three words of) research, and complex intervention. More KT terms were associated with KT application articles (n = 13) and KT theory articles (n = 18).</p> <p>Conclusions</p> <p>We collected 100 terms describing KT research. Authors used 46 of them in titles and abstracts of KT articles. Of these, approximately half discriminated between KT and non-KT articles. Thus, the need for consolidation and consistent use of fewer terms related to KT research is evident.</p

eHealth Implementation Issues in Low-Resource Countries: Model, Survey, and Analysis of User Experience

BackgroundThe implementation of eHealth in low-resource countries (LRCs) is challenged by limited resources and infrastructure, lack of focus on eHealth agendas, ethical and legal considerations, lack of common system interoperability standards, unreliable power, and shortage of trained workers. ObjectiveThe aim of this study is to describe and study the current situation of eHealth implementation in a small number of LRCs from the perspectives of their professional eHealth users. MethodsWe developed a structural equation model that reflects the opinions of professional eHealth users who work on LRC health care front lines. We recruited country coordinators from 4 LRCs to help recruit survey participants: India, Egypt, Nigeria, and Kenya. Through a web-based survey that focused on barriers to eHealth implementation, we surveyed 114 participants. We analyzed the information using a structural equation model to determine the relationships among the constructs in the model, including the dependent variable, eHealth utilization. ResultsAlthough all the model constructs were important to participants, some constructs, such as user characteristics, perceived privacy, and perceived security, did not play a significant role in eHealth utilization. However, the constructs related to technology infrastructure tended to reduce the impact of concerns and uncertainties (path coefficient=−0.32; P=.001), which had a negative impact on eHealth utilization (path coefficient=−0.24; P=.01). Constructs that were positively related to eHealth utilization were implementation effectiveness (path coefficient=0.45; P<.001), the countries where participants worked (path coefficient=0.29; P=.004), and whether they worked for privately or publicly funded institutions (path coefficient=0.18; P<.001). As exploratory research, the model had a moderately good fit for eHealth utilization (adjusted R2=0.42). ConclusionseHealth success factors can be categorized into 5 groups; our study focused on frontline eHealth workers’ opinions concerning 2 of these groups: technology and its support infrastructure and user acceptance. We found significant disparities among the responses from different participant groups. Privately funded organizations tended to be further ahead with eHealth utilization than those that were publicly funded. Moreover, participant comments identified the need for more use of telemedicine in remote and rural regions in these countries. An understanding of these differences can help regions or countries that are lagging in the implementation and use of eHealth technologies. Our approach could also be applied to detailed studies of the other 3 categories of success factors: short- and long-term funding, organizational factors, and political or legislative aspects

A scoping review of classification schemes of interventions to promote and integrate evidence into practice in healthcare

Abstract Background Many models and frameworks are currently used to classify or describe knowledge translation interventions to promote and integrate evidence into practice in healthcare. Methods We performed a scoping review of intervention classifications in public health, clinical medicine, nursing, policy, behaviour science, improvement science and psychology research published to May 2013 by searching MEDLINE, PsycINFO, CINAHL and the grey literature. We used five stages to map the literature: identifying the research question; identifying relevant literature; study selection; charting the data; collating, summarizing, and reporting results. Results We identified 51 diverse classification schemes, including 23 taxonomies, 15 frameworks, 8 intervention lists, 3 models and 2 other formats. Most documents were public health based, 55% included a literature or document review, and 33% were theory based. Conclusions This scoping review provides an overview of schemes used to classify interventions which can be used for evaluation, comparison and validation of existing and emerging models. The collated taxonomies can guide authors in describing interventions; adequate descriptions of interventions will advance the science of knowledge translation in healthcare

Challenges in eating disorder diagnosis and management among family physicians and trainees: a qualitative study

Background Family physicians are one of the first points of contact for individuals with eating disorders (EDs) seeking care and treatment, but training in this area is suboptimal and insufficient. Specialized ED treatment programs often have long wait lists, and family physicians are responsible for patients care in the interim. The aim of this study was to identify the learning needs and challenges faced by Canadian family physicians and trainees when caring for patients with EDs. Methods We recruited six family medicine residents and five family physicians practicing in an academic unit in the Department of Family Medicine of a medical school in urban southwestern Ontario, Canada. We used purposive sampling, focusing on residents and faculty physicians from the department and conducted one focus group for the residents and another for the faculty physicians, exploring their clinical knowledge and challenges when managing ED patients. The focus groups were audio-recorded and transcribed verbatim prior to thematic coding. Results Physicians and residents faced challenges in discussing, screening, and managing patients with EDs. Three themes that emerged from the qualitative data highlighted training needs related to: (a) improving communication skills when treating a patient with an ED, (b) more effective screening and diagnosis in primary care practice, and (c) optimizing management strategies for patients with an ED, especially patients who are waiting for more intensive treatment. A fourth theme that emerged was the distress experienced by family physicians as they try best to manage and access care for their patients with EDs. Conclusion Addressing the learning needs identified in this study through continuing education offerings could aid family physicians in confidently providing effective, evidence-based care to patients with EDs. Improvement in training and education could also alleviate some of the distress faced by family physicians in managing patients with EDs. Ultimately, system changes to allow more efficient and appropriate levels of care for patients with EDs, removing the burden from family medicine, are critical as EDs are on the rise. Plain English summary A person with an eating disorder will normally seek care from their family physician first. These conditions can dramatically reduce the quality of a person’s life and health. Family physicians therefore need to know how best to help these patients or refer them to a more intensive level of care, which often has long wait lists. We asked a group of family physicians and a group of family medicine trainees about their experiences with patients with eating disorders and about the information they wished they had to help these patients. The results show that they need more information on how to talk to a patient about eating disorders without judgement, how to diagnose a patient with an eating disorder, and then what treatment and management is needed while they wait for more intensive treatment for sicker patients. The physicians and trainees both talked about the stress and worry that they faced when treating patients with eating disorders. Besides their lack of training about these conditions, family physicians also described difficulties when trying to access timely specialized services for their patients. Physicians can experience moral distress when they know that their patients need higher level care, but there are systemic barriers to specialized programs that block their patients from getting the care they need when they need it.Medicine, Faculty ofNon UBCMedicine, Department ofReviewedFacultyResearche

Limits of search filter development

Objective: The research attempted to develop search filters for biomedical literature databases that improve retrieval of studies of clinical relevance for the nursing and rehabilitation professions. Methods: Diagnostic testing framework compared machine-culled and practitioner-nominated search terms with a hand-tagged clinical literature database. Results: We were unable to: (1) develop filters for nursing, likely because of the overlapping and expanding scope of practice for nurses in comparison with medical professionals, or (2) develop filters for rehabilitation, because of its broad scope and the profession’s multifaceted understanding of ‘‘health and ability.’’ Conclusions: We found limitations on search filter development for these health professions: nursing and rehabilitation