Setting measureable goals with young people : qualitative feedback from the Goal Attainment Scale in youth mental health

Introduction Measuring occupational performance is an essential part of clinical practice; however, there is little research on service user perceptions of measures. The aim of this investigation was to explore the acceptability and utility of one occupational performance outcome measure, Goal Attainment Scaling, with young people (12–25 years old) seeking psychological help. Method Semi-structured interviews were conducted with ten young people seeking help from a youth mental health clinic. Interviews were audio taped and a field diary kept. Interviews were transcribed verbatim and analysed using content analysis. Results were verified by member checking. Results All participants were able to engage in using Goal Attainment Scaling to set goals for therapy, and reported the process to be useful. The participants identified the physical location and ownership of the scale was important to help motivate them to work on their goals. Conclusion Young help-seekers see Goal Attainment Scaling as an acceptable tool to facilitate the establishment of functional goals. Young service users were particularly keen to maintain control over the physical location of completed forms.

Using automated text analysis to evaluate students’ conceptual understanding

Background: A major challenge for assessing students’ conceptual understanding of STEM subjects is the capacity of assessment tools to reliably and robustly evaluate student thinking and reasoning. Multiple-choice tests are typically used to assess student learning and are designed to include distractors that can indicate students’ incomplete understanding of a topic or concept based on which distractor the student selects. However, these tests fail to provide the critical information uncovering the how and why of students’ reasoning for their multiple-choice selections. Open-ended or structured response questions are one method for capturing higher level thinking, but are often costly in terms of time and attention to properly assess student responses. Purpose: The goal of this study is to evaluate methods for automatically assessing open-ended responses, e.g. students’ written explanations and reasoning for multiple-choice selections. Design/Method: We incorporated an open response component for an online signals and systems multiple-choice test to capture written explanations of students’ selections. The effectiveness of an automated approach for identifying and assessing student conceptual understanding was evaluated by comparing results of lexical analysis software packages (Leximancer and NVivo) to expert human analysis of student responses. In order to understand and delineate the process for effectively analysing text provided by students, the researchers evaluated strengths and weakness for both the human and automated approaches. Results: Human and automated analyses revealed both correct and incorrect associations for certain conceptual areas. For some questions, that were not anticipated or included in the distractor selections, showing how multiple-choice questions alone fail to capture the comprehensive picture of student understanding. The comparison of textual analysis methods revealed the capability of automated lexical analysis software to assist in the identification of concepts and their relationships for large textual data sets. We also identified several challenges to using automated analysis as well as the manual and computer-assisted analysis. Conclusions: This study highlighted the usefulness incorporating and analysing students’ reasoning or explanations in understanding how students think about certain conceptual ideas. The ultimate value of automating the evaluation of written explanations is that it can be applied more frequently and at various stages of instruction to formatively evaluate conceptual understanding and engage students in reflective

The perspectives of older drivers on the impact of feedback on their driving behaviours : a qualitative study

Self-regulation is a coping strategy that allows older drivers to drive safely for longer. Self-regulation depends largely on the ability of drivers to evaluate their own driving. Therefore the success of self-regulation, in terms of driving safety, is influenced by the ability of older drivers to have insight into their declining driving performance. In addition, previous studies suggest that providing feedback to older adults regarding their driving skills may lead them to change their driving behaviour. However, little is currently known about the impact of feedback on older drivers’ self-awareness and their subsequent driving regulatory behaviour. This study explored the process of self-regulation and driving cessation among older drivers using the PAPM as a framework. It also investigated older adults’ perceptions and opinions about receiving feedback in regards to their driving abilities. Qualitative focus groups with 27 participants aged 70 years or more were conducted. Thematic analysis resulted in the development of five main themes; the meaning of driving, changes in driving pattern, feedback, the planning process, and solutions. The analysis also resulted in an initial model of driving self-regulation among older drivers that is informed by the current research and the Precaution Adoption Process Model as the theoretical framework. It identifies a number of social, personal, and environmental factors that can either facilitate or hinder people’s transition between stages of change. The findings from this study suggest that further elaboration of the PAPM is needed to take into account the role of insight and feedback on the process of self-regulation among older drivers.

Automated melanoma diagnosis system

An automated melanoma diagnosis system, the so-called Skin Polar-probe, was developed to improve the chances of early detection of skin cancers and help save the lives of melanoma victims. The system will offer unique benefits to aid early detection of melanoma - the key to reducing deaths caused by this cancer.

Accelerating taxonomic discovery through automated character extraction

This paper discusses the following key messages. Taxonomy is (and taxonomists are) more important than ever in times of global change. Taxonomic endeavour is not occurring fast enough: in 250 years since the creation of the Linnean Systema Naturae, only about 20% of Earth's species have been named. We need fundamental changes to the taxonomic process and paradigm to increase taxonomic productivity by orders of magnitude. Currently, taxonomic productivity is limited principally by the rate at which we capture and manage morphological information to enable species discovery. Many recent (and welcomed) initiatives in managing and delivering biodiversity information and accelerating the taxonomic process do not address this bottleneck. Development of computational image analysis and feature extraction methods is a crucial missing capacity needed to enable taxonomists to overcome the taxonomic impediment in a meaningful time frame. Copyright © 2009 Magnolia Press.

Mentor feedback : models, viewpoints and strategies

A mentor’s feedback can present professional insights to allow a mentee to reflect and develop practice. This paper positions two models for feedback that have emanated from empirical studies. It also demonstrates the diverse viewpoints of mentors and suggests strategies for providing quality feedback. In one qualitative study, 24 mentors observed a final-year preservice teacher through a professionally video-recorded lesson and wrote their observations towards giving feedback to the potential mentee. Tables illustrated in the paper, show that mentors’ positive feedback and constructive criticisms vary considerably on the same observed events. Data from this study were synthesised to posit a theoretical model for analysing mentor feedback in an interconnected, three-way Venn diagram, namely: visual, auditory and conceptual frames. Another study (n=28), which is a collection of mentor teachers’ work samples during the Mentoring for Effective Teaching (MET) program, provides strategies within six feedback practices, that is: (1) negotiated mentor-mentee expectations for providing feedback on practices, (2) reviewing teaching plans, (3) arranging for observations of practices, (4) providing oral feedback, (5) providing written feedback, and; (6) presenting opportunities for the mentee to evaluate teaching practices with consideration of the mentor’s feedback. For example, on the last mentioned practice (6) there were strategies such as “Plan a time for evaluation of practices (guided reflection)”, “Read the mentee’s reflection on practice and discuss how it aligns with your observations of their practices”, and “Highlight verbally and/or in writing where the mentee is perceptive about the reflection and how the reflection could be enhanced for future evaluations”. Developing a range of strategies that may assist the mentee in professional growth, include enlisting a community of mentors, ensuring mentors have a repertoire of strategies for articulating feedback, and using mentor feedback tools and models. This study has implications for the development of feedback models and strategies.

Automated fit quantification of tibial nail designs during the insertion using computer three-dimensional modelling

Intramedullary nailing is the standard fixation method for displaced diaphyseal fractures of the tibia. An optimal nail design should both facilitate insertion and anatomically fit the bone geometry at its final position in order to reduce the risk of stress fractures and malalignments. Due to the nonexistence of suitable commercial software, we developed a software tool for the automated fit assessment of nail designs. Furthermore, we demonstrated that an optimised nail, which fits better at the final position, is also easier to insert. Three-dimensional models of two nail designs and 20 tibiae were used. The fitting was quantified in terms of surface area, maximum distance, sum of surface areas and sum of maximum distances by which the nail was protruding into the cortex. The software was programmed to insert the nail into the bone model and to quantify the fit at defined increment levels. On average, the misfit during the insertion in terms of the four fitting parameters was smaller for the Expert Tibial Nail Proximal bend (476.3 mm2, 1.5 mm, 2029.8 mm2, 6.5 mm) than the Expert Tibial Nail (736.7 mm2, 2.2 mm, 2491.4 mm2, 8.0 mm). The differences were statistically significant (p ≤ 0.05). The software could be used by nail implant manufacturers for the purpose of implant design validation.

Is there a bone-nail specific entry point? automated fit quantification of tibial nail designs during the insertion for six different nail entry points

Intramedullary nailing is the standard fixation method for displaced diaphyseal fractures of tibia. Selection of the correct nail insertion point is important for axial alignment of bone fragments and to avoid iatrogenic fractures. However, the standard entry point (SEP) may not always optimise the bone-nail fit due to geometric variations of bones. This study aimed to investigate the optimal entry for a given bone-nail pair using the fit quantification software tool previously developed by the authors. The misfit was quantified for 20 bones with two nail designs (ETN and ETN-Proximal Bend) related to the SEP and 5 entry points which were 5 mm and 10 mm away from the SEP. The SEP was the optimal entry point for 50% of the bones used. For the remaining bones, the optimal entry point was located 5 mm away from the SEP, which improved the overall fit by 40% on average. However, entry points 10 mm away from the SEP doubled the misfit. The optimised bone-nail fit can be achieved through the SEP and within the range of a 5 mm radius, except posteriorly. The study results suggest that the optimal entry point should be selected by considering the fit during insertion and not only at the final position.

Visual control for automated aircraft collision avoidance systems

This thesis presents a new vision-based decision and control strategy for automated aircraft collision avoidance that can be realistically applied to the See and Avoid problem. The effectiveness of the control strategy positions the research as a major contribution toward realising the simultaneous operation of manned and unmanned aircraft within civilian airspace. Key developments include novel classical and visual predictive control frameworks, and a performance evaluation technique aligned with existing aviation practise and applicable to autonomous systems. The overall approach is demonstrated through experimental results on a small multirotor unmanned aircraft, and through high fidelity probabilistic simulation studies.

Automated counting of the Northern Pacific Sea Star in the Derwent using shape recognition

A novel shape recognition algorithm was developed to autonomously classify the Northern Pacific Sea Star (Asterias amurenis) from benthic images that were collected by the Starbug AUV during 6km of transects in the Derwent estuary. Despite the effects of scattering, attenuation, soft focus and motion blur within the underwater images, an optimal joint classification rate of 77.5% and misclassification rate of 13.5% was achieved. The performance of algorithm was largely attributed to its ability to recognise locally deformed sea star shapes that were created during the segmentation of the distorted images.

Automated sensory data alignment for environmental and epidermal change monitoring

In this paper we present research adapting a state of the art condition-invariant robotic place recognition algorithm to the role of automated inter- and intra-image alignment of sensor observations of environmental and skin change over time. The approach involves inverting the typical criteria placed upon navigation algorithms in robotics; we exploit rather than attempt to fix the limited camera viewpoint invariance of such algorithms, showing that approximate viewpoint repetition is realistic in a wide range of environments and medical applications. We demonstrate the algorithms automatically aligning challenging visual data from a range of real-world applications: ecological monitoring of environmental change, aerial observation of natural disasters including flooding, tsunamis and bushfires and tracking wound recovery and sun damage over time and present a prototype active guidance system for enforcing viewpoint repetition. We hope to provide an interesting case study for how traditional research criteria in robotics can be inverted to provide useful outcomes in applied situations.