Parent Engagement in Supported Supervised Visitation in Child Welfare

Thesis (Master's)--University of Washington, 2016-06

Étude exploratoire sur le point de vue des enfants placés vivant l'expérience d'avoir des visites supervisées avec leurs parents

L’objectif de ce mémoire est de mieux comprendre, à partir de leur point de vue, le vécu des enfants âgés de 6 à 12 ans qui sont placés dans des ressources d’accueil et qui vivent l’expérience d’avoir des visites supervisées avec leurs parents. Pour ce faire, des entrevues semi-dirigées ont été réalisées auprès de douze enfants hébergés dans des unités de vie, foyers de groupe ou ressources intermédiaires du Centre jeunesse de Montréal – Institut Universitaire et du Centre jeunesse de la Montérégie. Les entrevues réalisées auprès des enfants ont été soumises à une analyse de contenu thématique. Les résultats de l’étude montrent que les enfants ne comprennent pas toujours bien les raisons qui justifient la mise en place de visites supervisées, ni le rôle des adultes dans les décisions, ni celui du tiers durant les visites. De façon générale, les enfants sont favorables au maintien des contacts avec leurs parents, mais sont plus critiques face aux cadres imposés par ces visites. Il ressort également de l’étude que les enfants sont très peu consultés en lien avec l’organisation et la planification de leurs visites et qu’ils souhaiteraient l’être davantage. Les enfants ont rapporté de nombreuses insatisfactions en lien avec les modalités organisationnelles des visites. L’analyse du discours qui a été menée a permis de mettre en évidence le fait que les visites supervisées sont une source de stress importante pour l’enfant. La création d’un guide d’information destiné aux enfants pour expliquer ce qu’est une visite supervisée, les raisons de sa mise en place, ses buts et ses objectifs serait une piste intéressante à explorer.

Étude exploratoire sur le point de vue des enfants placés vivant l'expérience d'avoir des visites supervisées avec leurs parents

L’objectif de ce mémoire est de mieux comprendre, à partir de leur point de vue, le vécu des enfants âgés de 6 à 12 ans qui sont placés dans des ressources d’accueil et qui vivent l’expérience d’avoir des visites supervisées avec leurs parents. Pour ce faire, des entrevues semi-dirigées ont été réalisées auprès de douze enfants hébergés dans des unités de vie, foyers de groupe ou ressources intermédiaires du Centre jeunesse de Montréal – Institut Universitaire et du Centre jeunesse de la Montérégie. Les entrevues réalisées auprès des enfants ont été soumises à une analyse de contenu thématique. Les résultats de l’étude montrent que les enfants ne comprennent pas toujours bien les raisons qui justifient la mise en place de visites supervisées, ni le rôle des adultes dans les décisions, ni celui du tiers durant les visites. De façon générale, les enfants sont favorables au maintien des contacts avec leurs parents, mais sont plus critiques face aux cadres imposés par ces visites. Il ressort également de l’étude que les enfants sont très peu consultés en lien avec l’organisation et la planification de leurs visites et qu’ils souhaiteraient l’être davantage. Les enfants ont rapporté de nombreuses insatisfactions en lien avec les modalités organisationnelles des visites. L’analyse du discours qui a été menée a permis de mettre en évidence le fait que les visites supervisées sont une source de stress importante pour l’enfant. La création d’un guide d’information destiné aux enfants pour expliquer ce qu’est une visite supervisée, les raisons de sa mise en place, ses buts et ses objectifs serait une piste intéressante à explorer.

Automated supervised classification of variable stars in the CoRoT programme Method and application to the first four exoplanet fields

Aims. In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. Methods. The basis of the adopted supervised classification methodology has been described in detail in a previous paper, as is its application to the OGLE database. Here, we present the modifications of the algorithms and of the training set to optimize the performance when applied to the CoRoT data. Results. Classification results are presented for the observed fields IRa01, SRc01, LRc01, and LRa01 of the CoRoT mission. Statistics on the number of variables and the number of objects per class are given and typical light curves of high-probability candidates are shown. We also report on new stellar variability types discovered in the CoRoT data. The full classification results are publicly available.

Ant visitation to extrafloral nectaries decreases herbivory and increases fruit set in Chamaecrista debilis (Fabaceae) in a Neotropical savanna

Studies of ant-plant relationships elucidate how top-down effects of the third trophic level can affect the biomass, richness, and/or species composition of plants. Although widespread in the neotropics, few studies have so far examined the direct effects of ants on plant fitness. Here, through experimental manipulation (ant-exclusion) under natural conditions, we examined the effect of ant visitation to extrafloral nectaries on leaf herbivory and fruit set in Chamaecrista debilis in the Brazilian savanna. As opposed to other Chamaecrista species, our results showed that visiting ants (15 species) significantly reduce herbivory and increase fruit set by more than 50% compared to plants from which ants were excluded. This mutualistic system is facultative in nature, and corroborates the potential beneficial role of exudate-feeding ants as anti-herbivore agents of tropical plants. (C) 2010 Elsevier GmbH. All rights reserved.

Attitudes of paediatric medical ward staff to a dog visitation programme

No previous studies regarding either structured, strictly controlled pet visitation programmes in paediatric hospital wards or hospital staff attitudes towards them have been conducted in Australia. Information regarding these is essential in order to minimize problems during implementation of such programmes. The aim of the study was to analyse hospital staff perceptions regarding the introduction of a pet visitation programme in an acute paediatric medical ward prior to and following implementation of the programme and to compare attitudes between the various professional groups. The study consisted of two cross-sectional surveys. A total of 224 anonymous questionnaires were distributed to administrators, doctors, nursing staff and therapists 6 weeks before and 195 were distributed 12 weeks after the introduction of a pet visitation programme. Responses were received from 115 respondents (before the programme introduction) and 45 respondents (after the programme introduction). Prior to the introduction of the dog visitation programme, there were high staff expectations that the programme would distract children from their illness, relax children and that it was a worthwhile project for the hospital to undertake. Following implementation of the programme these expectations were strongly endorsed, in addition to the perception that the ward was a happier place, the work environment was more interesting and that nurses accepted the dogs. After implementation staff were less concerned about the possibility of dog bites and dogs doing damage to equipment. Allied health staff and non-clinical staff were more positive about the programme with respect to ward climate and acceptance than were doctors and nurses. We conclude that well-planned dog visitation programmes result in positive anticipation among staff and high levels of satisfaction following programme impact.

A supervised versus home-based program effects on liver transplanted familial amyloidotic polyneuropathy patients: walking, fatigue and quality of life

Purpose: To evaluate the effects of a six months exercise training program on walking capacity, fatigue and health related quality of life (HRQL). Relevance: Familial amyloidotic polyneuropathy disease (FAP) is an autossomic neurodegenerative disease, related with systemic deposition of amyloidal fibre mainly on peripheral nervous system and mainly produced in the liver. FAP often results in severe functional limitations. Liver transplantation is used as the only therapy so far, that stop the progression of some aspects of this disease. Transplantation requires aggressive medication which impairs muscle metabolism and associated to surgery process and previous possible functional impairments, could lead to serious deconditioning. Reports of fatigue are common feature in transplanted patients. The effect of supervised or home-based exercise training programs in FAP patients after a liver transplant (FAPTX) is currently unknown.

A supervised versus home-based program effects on liver transplanted familial amyloidotic polyneuropathy patients: walking, fatigue and quality of life

Familial amyloidotic polyneuropathy is a systemic deposition of amyloidal fibre mainly on peripheral nervous system (but also in other systems like heart, gastrointestinal tract, kidneys, etc) and mainly produced in the liver. Purpose of this study: to evaluate the effects of a six months exercise training program(supervised or home-based) on walking capacity, fatigue and health related quality of life (HRQL) on Familial Amyloidotic Polyneuropathy patients submitted to a liver transplant.

Haptic robot-environment interaction for self-supervised learning in ground mobility

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

Partially supervised learning of fuzzy classification rules

Fuzzy classification, semi-supervised learning, data mining

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.