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We introduce a framework that predicts the goals behind observable human action in video. Motivated by evidence in developmental psychology, we leverage video of unintentional action to learn video representations of goals without direct supervision. Our approach models videos as contextual trajectories that represent both low-level motion and high-level action features. Experiments and visualizations show our trained model is able to predict the underlying goals in video of unintentional action. We also propose a method to "automatically correct" unintentional action by leveraging gradient signals of our model to adjust latent trajectories. Although the model is trained with minimal supervision, it is competitive with or outperforms baselines trained on large (supervised) datasets of successfully executed goals, showing that observing unintentional action is crucial to learning about goals in video. Project page: https://aha.cs.columbia.edu/

In this work, we attempt the segmentation of cardiac structures in late gadolinium-enhanced (LGE) magnetic resonance images (MRI) using only minimal supervision in a two-step approach. In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation. Each manually annotated ground truth provides labels of the myocardium and the left ventricle (LV) and right ventricle (RV) cavities, which are used as atlases. After multi-atlas label fusion by majority voting, we possess noisy labels for each of the targeted LGE images. A second set of manual labels exists for 30 patients of the target LGE CMR images, but are annotated on different MRI sequences (bSSFP and T2-weighted). Again, we use multi-atlas label fusion with a consistency constraint to further refine our noisy labels if additional annotations in other modalities are available for a given patient. In the second step, we train a deep convolutional network for semantic segmentation on the target data while using data augmentation techniques to avoid over-fitting to the noisy labels. After inference and simple post-processing, we achieve our final segmentation for the targeted LGE CMR images, resulting in an average Dice of 0.890, 0.780, and 0.844 for LV cavity, LV myocardium, and RV cavity, respectively.

This article considers the problem of correcting errors made by English as a Second Language writers from a machine learning perspective, and addresses an important issue of developing an appropriate training paradigm for the task, one that accounts for error patterns of non-native writers using minimal supervision. Existing training approaches present a trade-off between large amounts of cheap data offered by the native-trained models and additional knowledge of learner error patterns provided by the more expensive method of training on annotated learner data. We propose a novel training approach that draws on the strengths offered by the two standard training paradigms—of training either on native or on annotated learner data—and that outperforms both of these standard methods. Using the key observation that parameters relating to error regularities exhibited by non-native writers are relatively simple, we develop models that can incorporate knowledge about error regularities based on a small annotated sample but that are otherwise trained on native English data. The key contribution of this article is the introduction and analysis of two methods for adapting the learned models to error patterns of non-native writers; one method that applies to generative classifiers and a second that applies to discriminative classifiers. Both methods demonstrated state-of-the-art performance in several text correction competitions. In particular, the Illinois system that implements these methods ranked at the top in two recent CoNLL shared tasks on error correction. 1 We conduct further evaluation of the proposed approaches studying the effect of using error data from speakers of the same native language, languages that are closely related linguistically, and unrelated languages. 2

Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings. We try to incorporate contextual information from pre-trained language model to leverage annotation and benefit multilingual scenarios. Results show strong potential of Bidirectional Encoder Representations from Transformers (BERT) in grammatical error correction task.

The use of autonomous underwater vehicles is often hampered by stringent power constraints that limit the duration of their deployment. Here, we present a novel autonomous underwater charging system for robotic fish. The system features a charging station designed with form-fit claws to facilitate the robotic fish docking. A controller is implemented externally to monitor the battery level of the robotic fish, swimming autonomously in two dimensions. When the battery level is low, the controller commands the robotic fish to approach the charging station using video feedback from an overhead camera. The approach angle of the robotic fish to the charging station is optimized through a series of experiments, assessing the success of both docking and electrical contact, as well as the time to approach the charging station. To demonstrate the feasibility of the autonomous charging system, the robotic fish is monitored as it cycles through periods of charging and discharging while swimming in the tank. The proposed system is expected to find application in laboratory and educational settings, where robotic fish should operate with minimal supervision and assistance from technical staff over extended time periods.

The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled. In this work, we introduce a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model. To the best of our knowledge, this is the first method that exploits graph-based semi-supervised learning for X-ray data classification. Furthermore, we introduce a new multi-class classification functional with carefully selected class priors which allows for a smooth solution that strengthens the synergy between the limited number of labels and the huge amount of unlabelled data. We demonstrate, through a set of numerical and visual experiments, that our method produces highly competitive results on the ChestX-ray14 data set whilst drastically reducing the need for annotated data.

Cardiac rehabilitation is a program where the patient is supervised by a physical therapist while doing the exercise routine designed by their doctor. In the Philippines, a stand-alone equipment that can automate the doctor’s prescribed exercise routine while actively monitoring the patient’s vital sign is needed. The equipment could enable a safe and effective recovery with minimal supervision from the medical practitioners. This project aims to provide an automated aerobic exercise for cardiovascular rehabilitation patients through an adaptive cardiovascular training device. The automated exercise is based on the patient's heart rate which would be effectuated through the automatic adjustment of the load resistance of the cycling equipment. The equipment also includes blood pressure and oxygen saturation monitors to actively monitor the patient’s condition during the exercise.

Social media has become an inevitable part of individuals personal and business lives. Its benefits come with various negative consequences. One major concern is the prevalence of detrimental online behavior on social media, such as online harassment and cyberbullying. In this study, we aim to address the computational challenges associated with harassment detection in social media by developing a machine learning framework with three distinguishing characteristics. (1) It uses minimal supervision in the form of expert-provided key phrases that are indicative of bullying or non-bullying. (2) It detects harassment with an ensemble of two learners that co-train one another; one learner examines the language content in the message, and the other learner considers the social structure. (3) It incorporates distributed word and graph-node representations by training nonlinear deep models. The model is trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. We evaluate the effectiveness of our approach using post-hoc, crowdsourced annotation of Twitter, Ask.fm, and Instagram data, finding that our deep ensembles outperform previous non-deep methods for weakly supervised harassment detection.

Introduction Direct observation of procedural skills (DOPS) are tools designed by the Joint Advisory Group (JAG) to assess competence in endoscopy. These were expanded in July 2016 (new DOPS) to include those specific to paediatric gastroscopy (OGD). However, paediatric OGD DOPS assessments have not been validated. Methods We performed a prospective UK-wide analysis of formative paediatric OGD DOPS submitted to the JETS e-Portfolio over one-year (August 2016–2017). Scores were averaged across procedural domains (pre-procedural, procedural, post-procedural and endoscopic non-technical skills – ENTS), and according to skillset (cognitive or technical), and compared with the overall performance score using Spearman’s correlation. Overall scores were also compared by trainee grade. Results 157 DOPS assessments were completed by 20 unique trainers for 17 trainees. Overall performance score comprised: 1: Maximal supervision (4.5%), 2: Significant supervision (14.0%), 3: Minimal supervision (24.8%) and 4: Competent (56.7%). By domain, overall competence correlated most with mean scores for the ‘Insertion and Withdrawal’ domain (rho: 0.884, p Conclusions Competencies in paediatric OGD, as assessed within DOPS, vary in their correlation with overall competence. Strong correlations were reported for the ‘Insertion and Withdrawal’ domain, ‘Management of Complications’ items, and ‘’Technical’ skillsets. As assessors are completing the new DOPS in a consistent manner, with scores improving by seniority, this provides validity evidence for the new paediatric OGD DOPS.

As surgery residents graduate and begin their careers as junior attending surgeons, the question of whether a surgeon can complete a case alone still lingers. Allowing autonomy during residency answers this question. The purpose of this study was to gather input from general surgery residency program directors on how they achieve autonomy for residents in their programs. An online survey of 18 questions was sent to all general surgery residency program directors in the United States between April and June of 2013 via e-mail. Questions were asked regarding classification of autonomy, percentage of case completed by the resident independently, and in what area a resident worked with minimal supervision. Of the 202 delivered, 85 program directors were responded (42%). Seventy-eight per cent of programs classified a resident as surgeon junior whether the resident completed more than 50 per cent of the case. Most classified autonomy as either the resident completing >75 per cent of a case (41%) or completing the critical steps of a surgery (41%). Eighty-eight per cent stated that chief residents completed the majority of cases under supervision, whereas only 12 per cent stated the chief had autonomy in the operating room and also acted as teaching assistant. While, 60 per cent stated their chief residents did not work in any area of the hospital independently. Despite differences in how autonomy is defined among programs, most program directors feel that their chief residents do not achieve complete autonomy. Programs should allow their residents to work in a progressive responsibility as they progress into their fourth and fifth years of residency to achieve autonomy.

We present an approach to effectively use millions of images with noisy annotations in conjunction with a small subset of cleanly-annotated images to learn powerful image representations. One common approach to combine clean and noisy data is to first pre-train a network using the large noisy dataset and then fine-tune with the clean dataset. We show this approach does not fully leverage the information contained in the clean set. Thus, we demonstrate how to use the clean annotations to reduce the noise in the large dataset before fine-tuning the network using both the clean set and the full set with reduced noise. The approach comprises a multi-task network that jointly learns to clean noisy annotations and to accurately classify images. We evaluate our approach on the recently released Open Images dataset, containing ~9 million images, multiple annotations per image and over 6000 unique classes. For the small clean set of annotations we use a quarter of the validation set with ~40k images. Our results demonstrate that the proposed approach clearly outperforms direct fine-tuning across all major categories of classes in the Open Image dataset. Further, our approach is particularly effective for a large number of classes with wide range of noise in annotations (20-80% false positive annotations)., CVPR 2017

This study is a prospective case series analyzing the effects of a comprehensive lifestyle intervention program in three patients with chronic paraplegia having major risks for the cardiometabolic syndrome (CMS). Individuals underwent an intense 6-month program of circuit resistance exercise, nutrition using a Mediterranean diet and behavioral support, followed by a 6-month extension (maintenance) phase involving minimal support. The primary goal was a 7% reduction of body mass. Other outcomes analyzed insulin resistance using the HOMA-IR model, and plasma levels of fasting triglycerides and high-density lipoprotein cholesterol. All participants achieved the goal for 7% reduction of body mass and maintained the loss after the MP. Improvements were observed in 2/3 subjects for HOMA-IR and high-density lipoprotein cholesterol. All participants improved their risk for plasma triglycerides. We conclude, in a three-person case series of persons with chronic paraplegia, a lifestyle intervention program involving circuit resistance training, a calorie-restrictive Mediterranean-style diet and behavioral support, results in clinically significant loss of body mass and effectively reduced component risks for CMS and diabetes. These results were for the most part maintained after a 6-month MP involving minimal supervision.

In this era, the development of new technological is helping in simplifications of everyday processes including the industrial processes. One such application of technology is in the food industry for both in manufacturing and also in quality assessment and management of synthesized food products. In this field one of the major problems is contamination of both raw materials and products produced in the food industry. A number of chemical sensors are commercially available for field measurements of chemical species (e.g., portable gas chromatographs, surface-wave acoustic sensors, optical instruments), few have been adapted for use in environments for long-term monitoring or remediation applications due to the requirement of significant amounts of manual labor and supervision to operate. Instead, we seek low-cost sensors that have detection limit at very low level concentrations and operated with minimal supervision, which yield continuous real-time monitoring capabilities. In recent, nanotechnology has helped in the development of better contaminant sensors, i.e., sensors that can detect contaminations in food raw materials and food products. Biosensor is low-cost, quick, and reliable compared to existing methods and it can be modified to detect the presence of single or multiple substances using very small samples. Many of the biosensors were available for detecting and monitoring of toxins, contaminants, and residues in food products. Biosensors, also called molecular sensors, can detect the presence of specific chemicals, viruses, or molecules in air or liquids. Some of the sensors/materials like Wireless Networks, microwave coplanar, smart polymer, and other novel biosensors were used to detect the contamination in food products. This chapter presents a review of sensors and technologies that are capable of detecting and monitoring contaminant in the food industry. The review also focuses on the effect of pH, odor, taste, temperature, microbial nature, and other physicochemical parameters in the food products as well sensor quality and its life span. Along with effects, nano-encapsulated enzymes can be used to check and degrade the possible contaminants.

We present a novel approach to object classification and detection which requires minimal supervision and which combines visual texture cues and shape information learned from freely available unlabeled web search results. The explosion of visual data on the web can potentially make visual examples of almost any object easily accessible via web search. Previous unsupervised methods have utilized either large scale sources of texture cues from the web, or shape information from data such as crowdsourced CAD models. We propose a two-stream deep learning framework that combines these cues, with one stream learning visual texture cues from image search data, and the other stream learning rich shape information from 3D CAD models. To perform classification or detection for a novel image, the predictions of the two streams are combined using a late fusion scheme. We present experiments and visualizations for both tasks on the standard benchmark PASCAL VOC 2007 to demonstrate that texture and shape provide complementary information in our model. Our method outperforms previous web image based models, 3D CAD model based approaches, and weakly supervised models.

In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text available. Creating systems which can automatically extract important medical events and create disease progression timelines for users from such text can help in patient health monitoring as well as studying links between medical events and users’ participation in support groups. Prior work in this domain has used manually constructed keyword sets to detect medical events. In this work, our aim is to perform medical event detection using minimal supervision in order to develop a more general timeline construction system. Our system achieves an accuracy of 55.17%, which is 92% of the performance achieved by a supervised baseline system.

In this paper, we compare delexicalized transfer and minimally supervised parsing techniques on 32 different languages from Universal Dependencies treebank collection. The minimal supervision is in adding handcrafted universal grammatical rules for POS tags. The rules are incorporated into the unsupervised dependency parser in forms of external prior probabilities. We also experiment with learning this probabilities from other treebanks. The average attachment score of our parser is slightly lower then the delexicalized transfer parser, however, it performs better for languages from less resourced language families (non-Indo-European) and is therefore suitable for those, for which the treebanks often do not exist.

Objectives ACVS Diplomates were surveyed to identify a complete list of core surgical skills, determine the expected frequency of use of these skills, and ascertain the level of skills proficiency expected of entry-level practicing veterinarians. Study Design Internet-based survey. Sample Population Randomly chosen ACVS Diplomates (n = 750). Methods Rating scale items were used to construct the main portion of the survey with 26 skills that were scored by participants based on rankings of proficiency expected of entry-level veterinarians, as well as expected frequency of use of these skills. Additional demographic questions were included to gather information regarding gender, practice type, year of veterinary school, year of ACVS membership, job description, primary surgical discipline, practice location, average number of procedures performed on a weekly basis, and number of new graduates mentored in the last 5 years. Results Of 337 Diplomates (44.9%) who responded to the survey, >60% expected entry-level graduates to have good skills with minimal supervision or complete skills for 21 of the 26 skills categories listed. More than 60% of respondents ranked 7 skills with a lower frequency score (seldom or occasionally use). Orthopedic instrument handling and fixation skills as well as electrosurgical and laser skills received some of the lowest expected proficiency and frequency rankings. No additional skills categories were identified in the open-ended survey questions. Conclusions A complete list of core surgical skills was identified. There was broad consensus between boarded surgeons irrespective of their practice type, experience, or discipline for ranked proficiency and frequency scores among the core surgical skills expected of entry-level veterinarians.

There remains a lag in the development and implementation of effective strategies to serve lesbian, gay, bisexual, transgender, and questioning (LGBTQ) adolescents in schools. Although practitioners and scholars have begun to discuss how school counselors can provide effective services for LGBTQ students, there is minimal supervision literature to guide this work. This article presents an application of supervision that integrates LGBTQ Responsive School Counseling across the supervision of a comprehensive developmental school counseling program. Using the American School Counseling Association's National Model as a guideline, the authors provide a vehicle for supervisors to structure their work with professional school counselors.

A significant challenge that faces any teacher of introductory programming is the diversity of the class. At one extreme there will be students who have never programmed before, while at the other there will be students who have many years experience of programming. Handling this diversity is difficult. The temptation for the instructor is often to focus on the novice group and to assume that the others will get by with minimal supervision. This is understandable, but it can be risky. There is a very real risk that the neglected group of experienced programmers become bored and disengage from the course. At the worst, they can lose motivation and fail or drop out altogether. This paper describes and presents the outcomes of a project aimed at challenging the more experienced programmers in four introductory programming classes at four different UK institutions. The project took the form of a competition in which students were asked to devise and solve a series of programming challenges.

Entity classification, like many other important problems in NLP, involves learning classifiers over sparse highdimensional feature spaces that result from the conjunction of elementary features of the entity mention and its context. In this paper we develop a low-rank regularization framework for training maxentropy models in such sparse conjunctive feature spaces. Our approach handles conjunctive feature spaces using matrices and induces an implicit low-dimensional representation via low-rank constraints. We show that when learning entity classifiers under minimal supervision, using a seed set, our approach is more effective in controlling model capacity than standard techniques for linear classifiers.

Nearly all work in unsupervised grammar induction aims to induce unlabeled dependency trees from gold part-of-speechtagged text. These clean linguistic classes provide a very important, though unrealistic, inductive bias. Conversely, induced clusters are very noisy. We show here, for the first time, that very limited human supervision (three frequent words per cluster) may be required to induce labeled dependencies from automatically induced word clusters.

243 Background: Patients prescribed oral chemotherapy receive minimal supervision and support for adherence. We developed a smartphone mobile app consisting of medication reminders, symptom reporting, clinician communication, and educational resources for improving adherence and symptom management. In this study, we sought to identify potential correlates of app usage. Methods: From 12/2014 to 08/2016, 181 patients with diverse malignancies prescribed oral chemotherapy enrolled in a randomized trial to test the efficacy of a smartphone mobile app to improve adherence and symptoms. Medication adherence was monitored using electronic pill caps (MEMScaps). At baseline and 12 weeks, participants completed a measure of treatment satisfaction (FACIT-TS). We gathered clinical and treatment data from the electronic health record. Through the app we collected data on engagement: total number of minutes, days of usage, and number of completed symptom reports. We calculated Pearson correlations to identify associations between app engagement, using a latent variable for overall engagement, with adherence, treatment satisfaction and healthcare utilization. Results: 91 patients were randomly assigned to receive the mobile app. Over the 12-week study period, participants used the mobile app for a mean of 59 minutes (SD = 68) and 21.75 discrete days (SD = 21.24 days), completing 15.92 symptom reports (SD = 14.15) on average. Higher app engagement was associated with having a lower proportion of missed doses (r = -.248, p = .035) over the study period. Patients who became less satisfied with their communication with physicians (r = -.278, p = .019) and nurses (r = -.238, p = .047) used the app more. Higher app engagement was associated with fewer emergency department visits resulting in hospitalizations (r = -.252, p = .029). Conclusions: Among patients prescribed oral chemotherapy, higher usage of a mobile app targeting adherence was related to fewer missed doses and ED visits leading to hospitalization. Fostering patient engagement with the app may also serve to buffer difficulties in patient-clinician communication about care. Clinical trial information: NCT02157519.

As students progress through the midwifery programme, more and more study will be self-directed or independent—none more so than the dissertation which is likely to be the largest assessment, worked on over a number of months. Keeping on track can be difficult and a number of students leave this until the last minute due to poor planning or working on other pieces of work. The aim of this article is to give students hints and tips for keeping on track when working with minimal supervision.

The 2011/12 Antarctic field season saw the first use of a new British Antarctic Survey (BAS) ice-shelf hot-water drill system on the Larsen C and George VI ice shelves. Delivering 90 L min−1 at 80°C, a total of five holes >30 cm in diameter at three locations were successfully drilled through almost 400 m of ice to provide access to the underlying ocean, including the first access beneath the Larsen C ice shelf. These access holes enabled the deployment of instruments to measure sea-water conductivity, temperature, depth and microstructure, the collection of water samples and up to 2.9 m long sediment cores, before long-term oceanographic moorings were deployed. The simple modular design allowed for Twin Otter aircraft deployment, rapid assembly and commissioning of the system, which proved highly reliable with minimal supervision. A number of novel solutions to various operational sub-ice-shelf profiling and mooring deployment issues were successfully employed through the hot-water drilled access holes to aid the positioning, recovery and deployment of instruments. With future activities now focusing on the Filchner–Ronne Ice Shelf, the drill has been upgraded from its current 500 m capability to 1000 m with additional drill hose and further generator, pumping and heating modules.

Summary Background Minimal faculty member supervision of students refers to a method of instruction in which the patient–student encounter is not directly supervised by a faculty member, and presents a feasible solution in clinical teaching. It is unclear, however, how such practices are perceived by patients and how they affect student learning. Context We aimed to assess patient and medical student perceptions of clinical teaching with minimal faculty member supervision. Questionnaires focusing on the perception of students’ performance were administered to patients pre- and post-consultation. Students’ self-perceptions on their performance were obtained using a questionnaire at the end of the consultation. Innovation Before encounters with students, 22 per cent of the 95 patients were not sure if they would feel comfortable or trust the students; after the consultation, almost all felt comfortable (97%) and relied on the students (99%). The 81 students surveyed agreed that instruction with minimal faculty member supervision encouraged their participation and engagement (86%). They expressed interest in knowing patients’ opinions about their performance (94%), and they felt comfortable about being assessed by the patients (86%). Implications The minimal faculty member supervision model was well accepted by patients. Responses from the final-year students support the use of assessments that incorporate feedback from patients in their overall clinical evaluations.

This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small number of labeled examples. Our method achieves 16.5% and 11.3% F-1 score improvement over co-training on disease and virus NER respectively. We also show that by adding candidate phrase embeddings as features in a sequence tagger gives better performance compared to using word embeddings., Comment: In 14th Conference of the European Chapter of the Association for Computational Linguistic, 2014

Objective.To investigate the effect of workplace neck/shoulder strength training with and without regular supervision on neck/shoulder pain and headache among office workers.Method.A 20-week cluster randomized controlled trial among 351 office workers was randomized into three groups: two training groups with the same total amount of planned exercises three times per week (1) with supervision (3WS) throughout the intervention period, (2) with minimal supervision (3MS) only initially, and (3) a reference group (REF). Main outcome is self-reported pain intensity in neck and shoulder (scale 0–9) and headache (scale 0–10).Results.Intention-to-treat analyses showed a significant decrease in neck pain intensity the last 7 days in 3MS compared with REF: −0.5 ± 0.2(P<0.02)and a tendency for 3WS versus REF: −0.4 ± 0.2 (P<0.07). Intensity of headache the last month decreased in both training groups: 3WS versus REF: −1.1 ± 0.2 (P<0.001) and 3MS versus REF: −1.1 ± 0.2 (P<0.001). Additionally, days of headache decreased 1.0 ± 0.5 in 3WS and 1.3 ± 0.5 in 3MS versus REF. There were no differences between the two training groups for any of the variables.Conclusion.Neck/shoulder training at the workplace reduced neck pain and headache among office workers independently of the extent of supervision. This finding has important practical implications for future workplace interventions.

In many parts of the world, livestock guardian dogs (LGDs) are a relatively new and increasingly popular method for controlling the impact of wild predators on livestock. On large grazing properties in Australia, LGDs are often allowed to range freely over large areas, with minimal supervision by their owners. How they behave in this situation is mostly unknown. We fitted free-ranging Maremma sheepdogs with GPS tracking collars on three properties in Victoria, Australia; on two properties, four sheep were also fitted with GPS collars. We investigated how much time the Maremmas spent with their livestock, how far they moved outside the ranges of their stock, and tested whether they use their ranges sequentially, which is an effective way of maintaining a presence over a large area. The 95% kernel isopleth of the Maremmas ranged between 31 and 1161 ha, the 50% kernel isopleth ranged between 4 and 252 ha. Maremmas spent on average 90% of their time in sheep paddocks. Movements away from sheep occurred mostly at night, and were characterised by high-speed travel on relatively straight paths, similar to the change in activity at the edge of their range. Maremmas used different parts of their range sequentially, similar to sheep, and had a distinct early morning and late afternoon peak in activity. Our results show that while free-ranging LGDs spend the majority of their time with livestock, movements away from stock do occur. These movements could be important in allowing the dogs to maintain large territories, and could increase the effectiveness of livestock protection. Allowing LGDs to range freely can therefore be a useful management decision, but property size has to be large enough to accommodate the large areas that the dogs use.

The aim of this study is to develop a new Smart Heat Therapy device that is able to expedite the process, increase the effectiveness and reduce the limitations of existing devices used in physiotherapy procedure to stimulate aching muscle due to improper blood circulation within certain parts of the body. This new compact, portable, economical and user friendly device will promote blood circulation by continuously applying low level heat energy to the body to enlarge the blood vessel at targeted area with water as the heating medium. The advanced feature of the device is its capability to deliver consistent amount of heat energy required for selected duration of therapy session prescribed to the patient. Hence the patient can select the temperature and time required during therapy session. The vital feature of the device is the incorporation of safety features that makes it suitable to be used under minimal supervision either in infirmaries or at home.

Contains fulltext : 172046.pdf (Publisher’s version ) (Open Access) BACKGROUND: Entrustable Professional Activities (EPAs) are increasingly used as a focus for assessment in graduate medical education (GME). However, a consistent approach to guide EPA design is currently lacking, in particular concerning the actual content (knowledge, skills and attitude required for specific tasks) for EPAs. This paper describes a comprehensive five stage approach, which was used to develop two specialty-specific EPAs in emergency medicine focused on the first year of GME. METHODS: The five stage approach was used to gain consensus on the task, content and entrustment scale for two specialty-specific EPAs in emergency medicine. The participants consisted of twelve clinical supervisors working in the emergency department. The five stages were: 1) Selecting the EPA topic; 2) Developing the EPA content by collecting data from participants using focus group and individual interviews; 3) Drafting the EPAs based on analysis of collected data; 4) Seeking feedback on the draft EPAs from the participants and other stakeholders; 5) Refining and finalising the EPAs based on feedback. RESULTS: Two specialty-specific EPAs were developed using the five stage approach. The participants reached consensus on the specific tasks and criteria for performance for the two EPAs. They also agreed that both day-to-day (ad hoc) and formal (summative) entrustment decisions were put into practice through the intensity of supervision provided to PGY1 doctors. As a result, a three level entrustment and supervision scale consisting of direct active, indirect active, passive was developed reflecting the shift in the intensity of supervision from close supervision to minimal supervision. CONCLUSIONS: The five stage approach described in this paper was used successfully to develop two specialty-specific EPAs in emergency medicine along with a three level entrustment scale.We propose that the five stage approach is transferable to a range of medical training contexts to design specialty-specific EPAs.

Objective—To identify a list of core surgical skills and determine the frequency of use and proficiency in performance of these skills expected of entry-level veterinarians by general practitioners. Design—Mail-based survey. Sample—750 general practitioners randomly chosen from the AVMA membership database. Procedures—Survey respondents rated the proficiency and frequency of use expected of entry-level veterinarians in regard to 26 surgical skills. Demographic information (gender; graduation year; practice type, geographic location, and setting; number of veterinarians in practice; number of surgical procedures performed per week; and number of new graduates mentored in the past 5 years) of respondents was obtained. Results—387 (52%) general practitioners responded to the survey. Greater than 60% of respondents expected new graduates to have high proficiency and require minimal supervision for 21 of 26 skills. Greater than 60% of respondents assigned 6 of the skills a low expected frequency of use rating. Orthopedic skills, creation of square knots by use of a 1-handed tie technique, and use of electrosurgical and laser instruments received some of the lowest ratings. Conclusions and Clinical Relevance—Core surgical skills were identified. Results indicated a broad consensus among general practitioners independent of demographic characteristics. Results may aid veterinary colleges in identification of the surgical skills that are most important to include in surgical curricula and for which new graduates should attain proficiency according to general practitioners.

Objective: To identify factors influencing the retention of physiotherapists in one regional setting. Design: A qualitative framework using semi-structured interviews was used to collect data. Data were evaluated using thematic analysis. Setting: A regional city located on the coastline of Queensland, Australia, more than six hours drive from the capital city, Brisbane. Participants: Practicing physiotherapists employed by Queensland Health, the main public health employer. Main Outcome Measures: Retention themes identified from the interview transcripts. Results: The advantages of remaining employed in the regional city were centred on personal issues. Forty-four responses described personal advantages while 33 described professional advantages. The two most common advantages were remaining close to family and lifestyle and environment. Professional negative factors achieved a higher tally of 57 responses as compared to personal issues with only 24 responses. The two most common disadvantages described were professional issues including a lack of professional development and minimal supervision and support. All participants listed incentives requiring financial resourcing to improve retention. Conclusion: Negative work related factors were more significant when there is no personal connection to the community highlighting the importance addressing and maintaining a high level of professional satisfaction in order to retain clinicians.

Collecting epidemiological data from a large community sample has, in the past, been a costly and time-consuming exercise. This paper outlines a methodology for undertaking such research by using a set of standard psychiatric questionnaires, including a computerised version of the Diagnostic Interview Schedule Screening Interview on a portable laptop computer. The study of 1009 country South Australian subjects found that this technique provided prevalence estimates similar to those obtained from the more established Diagnostic Interview Schedule, with the added benefit that all interviews were successfully carried out by lay interviewers under minimal supervision, in the subject's home.

The paper describes the development of modern dynamic dense medium circuits which offer many oppurtunities for simplifying processes, for savings of costs and energy, and extending the reserves of ores which can be mined economically. Explanations are offered of the various features contributing to the high and consistent efficiency of two and three stage dense medium separators and the medium controls which have been developed for two and three product circuits. Logical designs and good materials of construction of the separators, effective circuits and the elimination of high wear items have made modern plants simple to operate and convenient for integration with continuously operating beneficiation circuits with minimal supervision. Examples are then given of an increasing range of operating plants and of new applications.

Laboratory experiments help students physically apply concepts proposed in lecture toward actual applications. With the involvement of senior civil engineering students, a Construction Materials Course has been restructured at Gonzaga University. This paper will illustrate how the newly designed course will enhance student learning in the Department of Civil Engineering at Gonzaga University within the Structural/Construction field. The new course will train students in the use of construction materials through lectures, site visits, literature research, and conducting experiments in the laboratory. With the addition of experimental procedures, students will respond better to the lectures, retaining more knowledge of concepts than in a traditional course setting. The course will also service other programs and be better integrated within the engineering curriculum, while emphasizing both technical and communication skills. This course differs from traditional laboratory classes, due to the fact that the students experience the highest level of active learning based on their responsibility to conduct a portion of the laboratory with minimal supervision. This paper is of interest to those who desire introducing a new course based on traditional teaching methodologies, complemented with nontraditional components, such as site visits, library literature research, and experiment design and performance.

Objective The purpose of this study was to determine the effectiveness of obstetrics simulator training for medical students by comparing measures of confidence in normal obstetrics skills of students with and without training. Study design After a lecture on normal labor and delivery, 33 third-year students practiced their skills either on an obstetrics simulator (n = 17) or received no further formal instruction (n = 16). All students were asked to respond to surveys of their experience and confidence in performing obstetrics procedures. Results Students who practiced deliveries on the simulator were more likely to believe that they could perform most portions of a vaginal delivery with minimal supervision or independently than were students who did not receive simulator experience. Fifteen students (88%) who received simulator experience felt that they were ready to attempt a vaginal delivery independently or with minimal supervision compared with 2 students (12.5%) who received a lecture only ( P Conclusion Students who practiced deliveries on an obstetrics simulator report higher levels of confidence in their skills to perform vaginal deliveries.

A difficult reality to face is that training may not be what it was. The majority of surgeons are graduates of the school of caffeine infused night shifts, staying up the next day and coping your way through an operation with minimal supervision. This ‘wholesome’ rite of passage for residents and registrars is disappearing – with vanishing hours, worker’s rights and the difficulties of explaining to patients how many hours the surgeon had worked before starting an operation that didn’t go that well. The feeling amongst most surgeons is still that without the hours how will trainee doctors match their forefathers for surgical skill and exposure. More and more commonly a figure of 30,000 hours of practice to make consultant is being bandied about theatre coffee rooms and committee meetings. Changes in training are certainly better for junior doctors but how exactly are craft specialities like orthopaedic surgery doing? …

Scenarios for manned missions to the Moon or Mars call for astronaut extravehicular teams to be accompanied by autonomous or semi-autonomous rovers. These rovers must be able to safely follow the astronauts over a variety of terrain with minimal assistance. We propose a color-based visual tracking system as a high-speed robust sensory approach for astronaut following and present a quantitative analysis of its accuracy based on a spectrum of known motion profiles. To promote robust operation during exceptional situations, a modal operations paradigm is presented that enables the rover to recover autonomously when possible and with minimal supervision when required. We have outfitted a mobile robot with a camera connected to our color tracking system and characterized its ability to follow the astronaut target over a variety of motion profiles under nominal and exceptional circumstances. Results show the rover is able to autonomously follow and re-acquire the astronaut in most cases and is able to safely stop and request assistance otherwise.

When we are born we do not know about sailing boats, frogs, cell-phones and wheelbarrows. By the time we reach school age we can easily recognize these categories of objects and many more using our visual system; by some estimates, we learn around 10 new categories per day with minimal supervision during the first few years of our lives. How can this happen? I will outline a computational approach to the problem of representing the visual properties of object categories, and of learning such models without supervision from cluttered images. Both static images of objects and dynamic displays such as the ones generated by human activity are handled by the theory. Its properties will be exemplified with experiments on a variety of categories.

The ability to understand medication and consistently follow a prescribed medical regimen varies among the general population. The Department of Nursing at Mohawk Valley Psychiatric Center developed a medication self-administration program to help consumers gain knowledge and/or improve their ability to participate in medication and symptoms management. Results have demonstrated that consumers can learn at least one more piece of information about their illness and/or medication despite their level of cognitive functioning, can learn to self-administer medications with minimal supervision before leaving the hospital, and can make an easier transition into the community setting.

EDITOR, - Hans-Jurgen Bartz and Winfried Kahlke point out that German doctors look for alternatives to work in Germany.1 As a German doctor working in Britain, I agree that the time spent as an “Arzt im Praktikum” is poorly paid, with doctors receiving only one third of the usual salary. They receive no structured training and are often expected to work with minimal supervision at an early stage, carrying out duties unacceptable for British house officers - for example, reporting electroencephalograms from their first day in neurology. The whole of this period can be spent in a single specialty and counts towards specialisation; it therefore does not provide any opportunity for gaining general experience or training outside that specialty. Bartz and Kahlke …

We describe the use of meshed local advancement flaps to repair surgical defects of the lower leg. The procedure produces a lattice of small cutaneous defects that heal rapidly by second intention. The technique is simple to perform and is suitable for large defects of the lower leg. Only minimal supervision is required postoperatively, and the wound causes virtually no pain.

ER , Channel 4, Wednesday 19 April at 9 pm E R (Emergency Room) , the US television drama of high adrenaline medical heroics, has blazed its way into a sixth series. It has undoubtedly captured the public imagination, with its strong characters, uneasy tensions, and frenetic trauma calls. It presents highly positive images of healthcare professionals, which is perhaps why the BMJ and Nursing Times put two ER characters on the cover of their recent joint issue. The programme reaches the masses. This year, in the week starting 18 January, it was watched in 21 million out of a possible99.4 million US households. In Britain Channel 4 screened two extraordinary episodes on 19 April, which caused much public excitement, sadness, and even grief. The country watched in horror the double stabbing of medical student Lucy and resident Dr Carter. In the first episode we saw Lucy working up the case of a young married man, Paul, who had presented with headaches. Carter offers minimal supervision. When the man becomes disoriented she …

A discussion is presented of three low-temperature reactor designs being pursued in the Soviet Union, Canada, and Sweden. By building reactors that operate at low temperatures, the governments and companies involved hope to reduce fears about safety, simplify the construction of the reactors, and reduce their costs. The aim is to produce energy at a cost substantially below that of the energy produced by oil-fired power plants and to use plants that need only minimal supervision, yet are safe enough to operate in populated regions.

The purpose of the work was to find whether parkinsonian patients persevere with L-dopa when under minimal supervision. A selected series of 15 parkinsonian patients were examined before and after 14

The SeDLE (Self-directed Learning Environment) Index is developed to measure the strength and ability of the learning environment to facilitate self-directed learning. This paper is descriptively explained the development and validation of the SeDLE index. Studies showed that a self-directed learning environment will produce a learner who is self-directed that can be a contributing factor to enhance the individual quality of life or at a workplace. Thus, this paper viewed the self-directed environment as a solution to support and inhibit the ability to function with minimal supervision that improve individual and group responsibilities among instructors and learners.

In establishing a chronic haemodialysis unit in Brunei the difficulties encountered were less insuperable than had been expected. Quite unsophisticated patients successfully dialysed unattended in some cases, or with minimal supervision in others.

Integrated Physiological Sciences is a second year course in Human Physiology, Biochemistry and Basic Pharmacology and is offered to an integrated class of students of medicine, dentistry, nutrition science and pharmacy. The Applied course was specifically designed to complement didactic lectures by assisting students to learn by controlled self discovery and problem-solving. Specific constraints are a student to staff ratio of approximately 240:6 and limited laboratory facilities.The Applied course is based on a structure of seven staggered cycles of 3 weeks each, with every cycle consisting of a formal practical (under lecturer supervision) in the first week, an open practical (practicals with minimal supervision or demonstration/exhibitions) in the second week and finally a problem-solving tutorial session (under lecturer direction, but not under strict/rigid control).Each lecturer is assigned two groups of approximately 20 students who he directs through each of the 21 weekly activities of the course....

Nine term and premature infants were discharged home while still being partly tubefed, and were successfully weaned onto full bottle or breast feeds by their mothers under minimal supervision.