1000 resultados para employment records


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Knowledge of milk transfer from mother to offspring and early solid food ingestions in mammals allows for a greater understanding of the factors affecting transition to nutritional independence and pre-weaning growth and survival. Yet studies monitoring suckling behaviour have often relied on visual observations, which might not accurately represent milk intake. We assessed the use of stomach temperature telemetry to monitor suckling and foraging behaviour in free-ranging harbour seal (Phoca vitulina) pups during lactation. Stomach temperature declines were analysed using principal component and cluster analyses, as well as trials using simulated stomachs resulting in a precise classification of stomach temperature drops into milk, seawater and solid food ingestions. Seawater and solid food ingestions represented on average 15.361.6% [0-40.0%] and 0.760.2% [0-13.0%], respectively, of individual ingestions. Overall, 63.7% of milk ingestions occurred while the pups were in the water, of which 13.9% were preceded by seawater ingestion. The average time between subsequent ingestions was significantly less for seawater than for milk ingestions. These results suggest that seawater ingestion might represent collateral ingestion during aquatic suckling attempts. Alternatively, as solid food ingestions (n = 19) were observed among 7 pups, seawater ingestion could result from missed prey capture attempts. This study shows that some harbour seals start ingesting prey while still being nursed, indicating that weaning occurs more gradually than previously thought in this species. Stomach temperature telemetry represents a promising method to study suckling behaviour in wild mammals and transition to nutritional independence in various endotherm species.

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The employment effect from raising the minimum wage has long been studied but remains in dispute. Our meta-analysis of 236 estimated minimum wage elasticities and 710 partial correlation coefficients from 16 UK studies finds no overall practically significant adverse employment effect. Unlike US studies, there seems to be little, if any, overall reporting bias. Multivariate meta-regression analysis identifies several research dimensions that are associated with differential employment effects. In particular, the residential home care industry may exhibit a genuinely adverse employment effect.

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We investigated whether being in temporary employment, as compared with permanent employment, was associated with a difference in Short Form 36 mental health and whether transitions from permanent employment to temporary employment were associated with mental health changes. We used fixed-effects regression in a nationally representative Australian sample with 10 waves of data collection (2001–2010). Interactions by age and sex were tested. Two forms of temporary employment were studied: “casual” (no paid leave entitlements or fixed hours) and “fixed-term contract” (a defined employment period plus paid leave). There were no significant mental health differences between temporary employment and permanent employment in standard fixed-effects analyses and no significant interactions by sex or age. For all age groups combined, there were no significant changes in mental health following transitions from stable permanent employment to temporary employment, but there was a significant interaction with age (P = 0.03) for the stable-permanent-to-casual employment transition, because of a small transition-associated improvement in mental health for workers aged 55–64 years (β = 1.61, 95% confidence interval: 0.34, 2.87; 16% of the standard deviation of mental health scores). Our analyses suggest that temporary employment is not harmful to mental health in the Australian context and that it may be beneficial for 55- to 64-year-olds transitioning from stable permanent employment to casual employment.

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Housing, employment and economic conditions in many nations have changed greatly over the past decades. This paper explores the ways in which changing housing markets, economic conditions and government policies have affected vulnerable individuals and households, using Australia as a case study. The paper finds a substantial number and proportion of low income Australians have been affected by housing and employment that is insecure with profound implications for vulnerability. Importantly, the paper suggests that in Australia the economic gains achieved as a consequence of mining-related growth in the early 2000s were translated as greater employment security for some on low incomes, but not all. Enhanced access to employment in this period was differentiated by gender, with women largely missing out on the growth in jobs. For the population as a whole, employment gains were offset by increased housing insecurity as accommodation costs rose. The paper finds low income lone parents were especially vulnerable because they were unable to benefit from a buoyant labour market over the decade 2000–2010. They were also adversely affected by national policy changes intended to encourage engagement with paid work. The outcomes identified for Australia are likely to have been mirrored in other nations, especially those that have embraced, or been forced to adopt, more restrictive welfare and income support regimes.

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The present paper uses a dynamic open-economy model with wage indexation to examine the impact of tourism on employment and welfare. Both short-run and long-run situations are analysed. It is well known that tourism converts non-traded goods into tradable goods. An increase in the demand for a non-traded good raises its relative price, which results in an expansion of the non-traded sector at the expense of the traded goods sector. This output shift raises labour employment in the short run. However, in the long run, the higher relative price leads to higher wages, resulting in a negative impact on labour employment. If the output effect is dominant, the expansion in tourism raises employment and welfare. However, under realistic conditions tourism may lower both labour employment and welfare due to rising costs. These results are demonstrated by simulating a dynamic model for the case of Hong Kong.

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Background: Unemployment and economic inactivity are associated with worse mental health in the general population, but there is limited understanding of whether these relationships are different for those persons with mental or physical disabilities. The aim of this study was to assess whether there were differences in mental health by labour force status among persons with and without disabilities. Method: Over eight annual waves of the Household, Income and Labour Dynamics in Australia (HILDA) survey, a total of 2379 people with disabilities and 11 417 people without disabilities were identified. Mental health using the Mental Component Summary (MCS) from the Short Form 36 was modelled as a function of labour force status using fixed-effects regression models to control for time invariant confounding. Differences between those with and without disabilities were assessed by including an interaction term in regression models. Results: After finding evidence of effect modification, regression models were stratified by disability status. After adjustment, unemployment and economic inactivity were associated with a -1.85 (95% CI -2.96 to -0.73, p<0.001) and -2.66 (95% CI -3.46 to -1.86, p<0.001) reduction in scores of the MCS among those with a disability. For those without a disability, there were smaller declines associated with unemployment (-0.57, 95% CI -1.02 to -0.12, p=0.013) and economic inactivity (-0.34, 95% CI -0.64 to 0.05, p=0.022). Conclusions: These results suggest a greater reduction in mental health for those persons with disabilities who were unemployed or economically inactive than those who were employed. This highlights the value of employment for people with disabilities.

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Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes.