983 resultados para retirement support model


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The article discusses a new decision support process for forestry pest management. Over the past few years, DSS have been introduced for forestry pest management, providing forest growers with advice in areas such as selecting the most suitable pesticide and relevant treatment. Most of the initiatives process knowledge from various domains for providing support for specific decision making problems. However, very few studies have identified the requirements of developing a combined process model in which all relevant practitioners can contribute and share knowledge for effective decision making; such an approach would need to include the decision makers’ perspective along with other relevant attributes such as the problem context and relevant policies. We outline a decision support process for forestry pest management, based on the design science research paradigm, in which a focus group technique has application to acquire both expert and practical knowledge in order to construct the DSS solution.

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The strong tendency of elderly employees to retire early and the simultaneous aging of the population have been major topics of policy and scientific debate. A key concern has been the financing of future pension schemes and possible labour shortage, especially in social and health services within the public sector. The aging of the population is inevitable, but efforts can be made to prevent or postpone early exit from the labour force, e.g., by identifying and intervening in the factors that contribute to the process of early retirement due to disability. The associations of intentions to retire early, poor mental health and different psychosocial factors with the process of disability retirement are still poorly understood. The purpose of this study was to investigate the associations of intentions to retire early, poor mental health, work and family related psychosocial factors and experiences of earlier life stages with the process of disability retirement. The data were derived from the Helsinki Health Study (HHS, N=8960) and the Health and Social Support Study (HeSSup, N=25 901). The Helsinki Health Study is an ongoing employee cohort study among middle-aged women and men. The Health and Social Support Study is an ongoing longitudinal study of a working-age sample representative of the Finnish population. The analyses were restricted to respondents 40 years of age or older. Age and gender adjusted prevalence and incidence rates were calculated. Associations were studied by using logistic, multinomial and Cox regression. Strong intentions to retire early were common among employees. Poor mental health, unfavourable working conditions and work-to-family conflicts were clearly associated with increased intentions to retire early. Strong intentions to retire early predicted disability retirement. Risk of disability retirement increased in a dose-response manner with increasing number of childhood adversities. Poor mental and somatic health, life dissatisfaction, heavy alcohol consumption, current smoking, obesity and low socioeconomic status were also predictors of disability retirement. The impact of poor mental health and adverse experiences from earlier life stages, work and family related psychosocial factors, e.g., work-family interface, the subjective experience of well-being and health related risk behaviours on the process of disability retirement should be recognised. Preventive measures against disability retirement should be launched before subjective experience of ill health, work disability and strong intentions to retire early emerge.

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Due to the improved prognosis of many forms of cancer, an increasing number of cancer survivors are willing to return to work after their treatment. It is generally believed, however, that people with cancer are either unemployed, stay at home, or retire more often than people without cancer. This study investigated the problems that cancer survivors experience on the labour market, as well as the disease-related, sociodemographic and psychosocial factors at work that are associated with the employment and work ability of cancer survivors. The impact of cancer on employment was studied combining the data of Finnish Cancer Registry and census data of the years 1985, 1990, 1995 or 1997 of Statistics Finland. There were two data sets containing 46 312 and 12 542 people with cancer. The results showed that cancer survivors were slightly less often employed than their referents. Two to three years after the diagnosis the employment rate of the cancer survivors was 9% lower than that of their referents (64% vs. 73%), whereas the employment rate was the same before the diagnosis (78%). The employment rate varied greatly according to the cancer type and education. The probability of being employed was greater in the lower than in the higher educational groups. People with cancer were less often employed than people without cancer mainly because of their higher retirement rate (34% vs. 27%). As well as employment, retirement varied by cancer type. The risk of retirement was twofold for people having cancer of the nervous system or people with leukaemia compared to their referents, whereas people with skin cancer, for example, did not have an increased risk of retirement. The aim of the questionnaire study was to investigate whether the work ability of cancer survivors differs from that of people without cancer and whether cancer had impaired their work ability. There were 591 cancer survivors and 757 referents in the data. Even though current work ability of cancer survivors did not differ between the survivors and their referents, 26% of cancer survivors reported that their physical work ability, and 19% that their mental work ability had deteriorated due to cancer. The survivors who had other diseases or had had chemotherapy, most often reported impaired work ability, whereas survivors with a strong commitment to their work organization, or a good social climate at work, reported impairment less frequently. The aim of the other questionnaire study containing 640 people with the history of cancer was to examine extent of social support that cancer survivors needed, and had received from their work community. The cancer survivors had received most support from their co-workers, and they hoped for more support especially from the occupational health care personnel (39% of women and 29% of men). More support was especially needed by men who had lymphoma, had received chemotherapy or had a low education level. The results of this study show that the majority of the survivors are able to return to work. There is, however, a group of cancer survivors who leave work life early, have impaired work ability due to their illness, and suffer from lack of support from their work place and the occupational health services. Treatment-related, as well as sociodemographic factors play an important role in survivors' work-related problems, and presumably their possibilities to continue working.

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Socio-economic and demographic changes among family forest owners and demands for versatile forestry decision aid motivated this study, which sought grounds for owner-driven forest planning. Finnish family forest owners’ forest-related decision making was analyzed in two interview-based qualitative studies, the main findings of which were surveyed quantitatively. Thereafter, a scheme for adaptively mixing methods in individually tailored decision support processes was constructed. The first study assessed owners’ decision-making strategies by examining varying levels of the sharing of decision-making power and the desire to learn. Five decision-making modes – trusting, learning, managing, pondering, and decisive – were discerned and discussed against conformable decision-aid approaches. The second study conceptualized smooth communication and assessed emotional, practical, and institutional boosters of and barriers to such smoothness in communicative decision support. The results emphasize the roles of trust, comprehension, and contextual services in owners’ communicative decision making. In the third study, a questionnaire tool to measure owners’ attitudes towards communicative planning was constructed by using trusting, learning, and decisive dimensions. Through a multivariate analysis of survey data, three owner groups were identified as fusions of the original decision-making modes: trusting learners (53%), decisive learners (27%), and decisive managers (20%). Differently weighted communicative services are recommended for these compound wishes. The findings of the studies above were synthesized in a form of adaptive decision analysis (ADA), which allows and encourages the decision-maker (owner) to make deliberate choices concerning the phases of a decision aid (planning) process. The ADA model relies on adaptability and feedback management, which foster smooth communication with the owner and (inter-)organizational learning of the planning institution(s). The summarized results indicate that recognizing the communication-related amenity values of family forest owners may be crucial in developing planning and extension services. It is therefore recommended that owners, root-level planners, consultation professionals, and pragmatic researchers collaboratively continue to seek stable change.

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The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

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Latent class and genetic analyses were used to identify subgroups of migraine sufferers in a community sample of 6,265 Australian twins (55% female) aged 25-36 who had completed an interview based on International Headache Society (IHS) criteria. Consistent with prevalence rates from other population-based studies, 703 (20%) female and 250 (9%) male twins satisfied the IHS criteria for migraine without aura (MO), and of these, 432 (13%) female and 166 (6%) male twins satisfied the criteria for migraine with aura (MA) as indicated by visual symptoms. Latent class analysis (LCA) of IHS symptoms identified three major symptomatic classes, representing 1) a mild form of recurrent nonmigrainous headache, 2) a moderately severe form of migraine, typically without visual aura symptoms (although 40% of individuals in this class were positive for aura), and 3) a severe form of migraine typically with visual aura symptoms (although 24% of individuals were negative for aura). Using the LCA classification, many more individuals were considered affected to some degree than when using IHS criteria (35% vs. 13%). Furthermore, genetic model fitting indicated a greater genetic contribution to migraine using the LCA classification (heritability, h(2)=0.40; 95% CI, 0.29-0.46) compared with the IHS classification (h(2)=0.36; 95% CI, 0.22-0.42). Exploratory latent class modeling, fitting up to 10 classes, did not identify classes corresponding to either the IHS MO or MA classification. Our data indicate the existence of a continuum of severity, with MA more severe but not etiologically distinct from MO. In searching for predisposing genes, we should therefore expect to find some genes that may underlie all major recurrent headache subtypes, with modifying genetic or environmental factors that may lead to differential expression of the liability for migraine.

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The aims of this study were to examine how workers' negative age stereotypes (i.e., denying older workers' ability to develop) and negative meta-stereotypes (i.e., beliefs that the majority of colleagues feel negative about older workers) are related to their attitudes towards retirement (i.e., occupational future time perspective and intention to retire), and whether the strength of these relationships is influenced by workers' self-categorization as an “older” person. Results of a study among Dutch taxi drivers provided mixed support for the hypotheses. Negative meta-stereotypes, but not negative age stereotypes, were associated with fewer perceived opportunities until retirement and, in turn, a stronger intention to retire. Self-categorization moderated the relationships between negative age (meta-)stereotypes and occupational future time perspective. However, contrary to expectations, the relations were stronger among workers with a low self-categorization as an older person in comparison with workers with a high self-categorization in this regard. Overall, results highlight the importance of psychosocial processes in the study of retirement intentions and their antecedents.

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It is crucial to advance understanding of the concept of successful aging at work to guide rigorous future research and effective practice. Drawing on the gerontology and life-span developmental literatures, I recently proposed a definition and theoretical framework of successful aging at work that revolve around employees increasingly deviating from average developmental trajectories across the working life span. Based on sustainability, person–job fit, and proactivity theories, Kooij suggested an alternative perspective that emphasizes the active role of employees for successful aging at work. In this article, I compare the 2 approaches and attempt a partial integration. I highlight the importance of a precise definition, comprehensive model, and critical discussion of successful aging at work. Furthermore, I suggest that person–environment fit variables other than person–job fit (e.g., person–organization fit) and adapting to person–environment misfit may also contribute to successful aging at work. Finally, I argue that proactive behaviors must have age-differential effects on work outcomes to be considered personal resources for successful aging at work.

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In the past decade, the Finnish agricultural sector has undergone rapid structural changes. The number of farms has decreased and the average farm size has increased when the number of farms transferred to new entrants has decreased. Part of the structural change in agriculture is manifested in early retirement programmes. In studying farmers exit behaviour in different countries, institutional differences, incentive programmes and constraints are found to matter. In Finland, farmers early retirement programmes were first introduced in 1974 and, during the last ten years, they have been carried out within the European Union framework for these programmes. The early retirement benefits are farmer specific and de-pend on the level of pension insurance the farmer has paid over his active farming years. In order to predict the future development of the agricultural sector, farmers have been frequently asked about their future plans and their plans for succession. However, the plans the farmers made for succession have been found to be time inconsistent. This study estimates the value of farmers stated succession plans in predicting revealed succession decisions. A stated succession plan exists when a farmer answers in a survey questionnaire that the farm is going to be transferred to a new entrant within a five-year period. The succession is revealed when the farm is transferred to a suc-cessor. Stated and revealed behaviour was estimated as a recursive Binomial Probit Model, which accounts for the censoring of the decision variables and controls for a potential correlation between the two equations. The results suggest that the succession plans, as stated by elderly farmers in the questionnaires, do not provide information that is significant and valuable in predicting true, com-pleted successions. Therefore, farmer exit should be analysed based on observed behaviour rather than on stated plans and intentions. As farm retirement plays a crucial role in determining the characteristics of structural change in agriculture, it is important to establish the factors which determine an exit from farming among eld-erly farmers and how off-farm income and income losses affect their exit choices. In this study, the observed choice of pension scheme by elderly farmers was analysed by a bivariate probit model. Despite some variations in significance and the effects of each factor, the ages of the farmer and spouse, the age and number of potential successors, farm size, income loss when retiring and the location of the farm together with the production line were found to be the most important determi-nants of early retirement and the transfer or closure of farms. Recently, the labour status of the spouse has been found to contribute significantly to individual retirement decisions. In this study, the effect of spousal retirement and economic incentives related to the timing of a farming couple s early retirement decision were analysed with a duration model. The results suggest that an expected pension in particular advances farm transfers. It was found that on farms operated by a couple, both early retirement and farm succession took place more often than on farms operated by a single person. However, the existence of a spouse delayed the timing of early retirement. Farming couples were found to co-ordinate their early retirement decisions when they both exit through agricultural retirement programmes, but such a co-ordination did not exist when one of the spouses retired under other pension schemes. Besides changes in the agricultural structure, the share and amount of off-farm income of a farm family s total income has also increased. In the study, the effect of off-farm income on farmers retirement decisions, in addition to other financial factors, was analysed. The unknown parameters were first estimated by a switching-type multivariate probit model and then by the simulated maxi-mum likelihood (SML) method, controlling for farmer specific fixed effects and serial correlation of the errors. The results suggest that elderly farmers off-farm income is a significant determinant in a farmer s choice to exit and close down the farm. However, off-farm income only has a short term effect on structural changes in agriculture since it does not significantly contribute to the timing of farm successions.

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Demographic changes give rise to an increasing number of middle-aged employees providing home-based care to an elderly family member. However, the potentially important role of employees' perceptions of organizational support for eldercare has so far not been investigated. The goal of this study was to examine a stressor–strain–outcome model (Koeske & Koeske, 1993) of eldercare strain as a mediator of the relationship between eldercare demands and caregivers' work engagement. Perceived organizational eldercare support was expected to attenuate the positive relationship between eldercare demands and eldercare strain and to buffer the negative relationship between eldercare strain and work engagement. Results of mediation and moderated mediation analyses with data collected from 147 employees providing eldercare supported the hypotheses. The findings suggest that perceived organizational eldercare support is especially beneficial for employees' work engagement when eldercare demands and strain are high.

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It has been argued that transition points in life, such as the approach towards, and early years of retirement present key opportunities for interventions to improve the health of the population. Research has also highlighted inequalities in health status in the retired population and in response to interventions which should be addressed. We aimed to conduct a systematic review to synthesise international evidence on the types and effectiveness of interventions to increase physical activity among people around the time of retirement. A systematic review of literature was carried out between February 2014 and April 2015. Searches were not limited by language or location, but were restricted by date to studies published from 1990 onwards. Methods for identification of relevant studies included electronic database searching, reference list checking, and citation searching. Systematic search of the literature identified 104 papers which described study populations as being older adults. However, we found only one paper which specifically referred to their participants as being around the time of retirement. The intervention approaches for older adults encompassed: training of health care professionals; counselling and advice giving; group sessions; individual training sessions; in-home exercise programmes; in-home computer-delivered programmes; in-home telephone support; in-home diet and exercise programmes; and community-wide initiatives. The majority of papers reported some intervention effect, with evidence of positive outcomes for all types of programmes. A wide range of different measures were used to evaluate effectiveness, many were self-reported and few studies included evaluation of sedentary time. While the retirement transition is considered a significant point of life change, little research has been conducted to assess whether physical activity interventions at this time may be effective in promoting or maintaining activity, or reducing health inequalities. We were unable to find any evidence that the transition to retirement period was, or was not a significant point for intervention. Studies in older adults more generally indicated that a range of interventions might be effective for people around retirement age.

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AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.

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Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from -24 to -94 depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.

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Standards for farm animal welfare are variously managed at a national level by government-led regulatory control, by consumer-led welfare economics and co-regulated control in a partnership between industry and government. In the latter case the control of research to support animal welfare standards by the relevant industry body may lead to a conflict of interest on the part of researchers, who are dependent on industry for continued research funding. We examine this dilemma by reviewing two case studies of research published under an Australian co-regulated control system. Evidence of unsupported conclusions that are favourable to industry is provided, suggesting that researchers do experience a conflict of interest that may influence the integrity of the research. Alternative models for the management of research are discussed, including the establishment of an independent research management body for animal welfare because of its public good status and the use of public money derived from taxation, with representation from government, industry, consumers, and advocacy groups.

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Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.