Systematic work environment management : experiences from implementation in Swedish small-scale enterprises

Small-scale enterprises face difficulties in fulfilling the regulations for organising Systematic Work Environment Management. This study compared three groups of small-scale manufacturing enterprises with and without support for implementing the provision. Two implementation methods, supervised and network method, were used. The third group worked according to their own ideas. Twenty-three enterprises participated. The effects of the implementation were evaluated after one year by semi-structured dialogue with the manager and safety representative. Each enterprise was classified on compliance with ten demands concerning the provision. The work environment was estimated by the WEST-method. Impact of the implementation on daily work was also studied. At the follow-up, the enterprises in the supervised method reported slightly more improvements in the fulfilment of the demands in the provision than the enterprises in the network method and the enterprises working on their own did. The effect of the project reached the employees faster in the enterprises with the supervised method. In general, the work environment improved to some extent in all enterprises. Extensive support to small-scale enterprises in terms of advise and networking aimed to fulfil the regulations of Systematic Work Environment Management had limited effect especially considering the cost of applying these methods.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.