A qualitative study of HRM programmes in eight different organizations was set up in order to identify factors, called implementability levers, that contributed to the implementability of those programmes. Three types of those levers were found, related to, respectively, the proces of the programme implementation (example: the involvement of line managers in the programme development), the content of the programme (example: the adaptibility of the programme) and the programme’s context (example: the accessibility of the HRM department for involved line managers). Levers in each of the categories appeared to have, as regards their impact on the programme’s implementability, a bright as well as a dark side: they tended to promote, in some specific way, as well as to hamper, in another specific way, the implementation of programmes. Taking care of programme implementability thus shows up as a doable, but puzzling, change management-like task of HR managers.
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Evidence concerning psychosocial interventions for children and young people with externalizing behavior problems has amassed at an impressive pace in recent years. Interventions that have been proven effective are now considered vehicles through which the knowledge of “what works” can be applied in practice. Outcomes for children, young people, and their families, however, have not improved in line with these advances in knowledge. This difference between the knowledge of “what works” and the application of this knowledge in real-life practice has become known as the “implementation gap”. This dissertation explores questions considering the implementation gap, with a focus on whether professionals are delivering the interventions as intended (treatment integrity).The results of the research underlying this dissertation show that 1) although measuring treatment integrity is important, it is often missing or not examined under adequate circumstances in studies, 2) applying interventions with a high level of treatment integrity makes a real difference to the end-users of the services and 3) targeted and continued support to professionals with a focus on providing feedback on levels of treatment integrity is necessary to enable them to deliver interventions as intended. Organizing support around common factors of interventions can be a first step in integrating and providing feasible support for professionals that provide more than one intervention.
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Presentatie gegeven over de review in Brussel Objectives: In the past decades many psychosocial interventions for elderly people with dementia have been developed and implemented. Relatively little research has been done on the extent to which these interventions were implemented in the daily care. The aim of this study was to obtain insight into strategies for successful implementation of psychosocial interventions in the daily residential dementia care. Using a modified RE-AIM framework, the indicators that are considered important for effective and sustainable implementation were defined.
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In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
We onderzoeken hoe bestaande, effectieve, valpreventie-programma’s goed kunnen worden geïmplementeerd. Dit doen we samen met belangrijke stakeholders, zoals thuiswonende ouderen met een verhoogd valrisico, fysiotherapeuten, wijkverpleegkundigen, apothekers, welzijnswerkers en beleidsmakers.
