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ON MAY 1ST, 1766, diligent newspaper readers, perusing the front pages of their favourite journals over breakfast, noticed a brief announcement amongst the plethora of advertisements for miracle cures and infallible nostrums. It read: ‘This day is published: elegantly printed on fine medium writing‐paper, in quarto, price five shilings, The New Bath Guide: or Memoirs of the B—n—r—d family. In a series of poetical epistles … Sold by J. Dodsley in Pall Mall’.

SIR ALAN HERBERT writing in The Spectator of 8th January, 1960, puts forward a point of view that librarians will find provocative, to say the least.

Current national policies present a challenge to the existing mental health workforce as most staff have not been trained to work with people within the context of their social support network. This paper presents two complementary training initiatives designed to enable mental health staff to meet the range of needs of families: (1) an in‐house accredited (one‐year) course that has enabled the successful creation of specialist family intervention in psychosis teams; and (2) a whole‐team trust‐wide training programme (three‐day course) to promote partnership working with families by both community and inpatient teams. Issues that have enabled the successful translation of training to practice are considered.

Mental health service user involvement in education has gained momentum and prominence over the past decade, but service user involvement in the assessment of students' practice remains underdeveloped. This paper reports findings from a qualitative analysis of documentary data that captured service users' feedback to mental health student nurses about their practice. Third year mental health nursing students in acute inpatient placements were required to elicit, record and reflect on the feedback that service users gave them about their practice.One hundred and eighty eight accounts of this feedback were analysed and findings are presented in terms of the methods that students used to gain feedback and the issues that emerged from this. The analysis also explored the role that students appear to play in care delivery and what aspects of their role service users most valued. The impact that the feedback had on the students' learning and practice is examined and discussed in relation to future opportunities for, and likely barriers to, continued service user involvement in assessing students' practice.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

SOCIAL scientists have not yet been able to formulate any general laws about behaviour in industry that are capable of broad application. In recent years, however, they have made many useful case studies of which the one just published by the Department of Scientific and Industrial Research is typical. It is an approach to the problem which can do much to increase the understanding of the way in which people react to common industrial situations.

In order to succeed in an action under the Equal Pay Act 1970, should the woman and the man be employed by the same employer on like work at the same time or would the woman still be covered by the Act if she were employed on like work in succession to the man? This is the question which had to be solved in Macarthys Ltd v. Smith. Unfortunately it was not. Their Lordships interpreted the relevant section in different ways and since Article 119 of the Treaty of Rome was also subject to different interpretations, the case has been referred to the European Court of Justice.

Following the publication of the Mallinson Report in March 1994, the Royal Institution of Chartered Surveyors undertook a survey of Valuation Practice in the UK in accordance with Recommendation 25 of the report. In January 1995 questionnaires were sent out to a cross‐section of practitioners in the market. The responses represented a wide range of experience, geographical location and type of work. The majority of respondents spent more than 50 per cent of their time undertaking valuations. The questionnaire distinguished between valuations undertaken for pricing (asset valuations, sale/purchase advice, CPO, etc.) and calculations of worth, where the role of the valuer was to analyse the investment for a specific purpose for a specific client. Respondents were asked to indicate whether they tended to adopt a “traditional” model or a “DCF” model for the valuation of a number of different types of investment. These were rack‐rented freeholds, reversionary freeholds, over‐rented property, short leaseholds and long leaseholds. In the replies there was a consensus opinion that, when undertaking calculations of worth, the DCF method should be used as the principal method but tempered with the use of traditional techniques alongside. Conversely, when pricing property the traditional method was considered to be the appropriate tool, although many respondents said that they would also use DCF techniques on the more “unusual” interests such as over‐rented. With regard to the actual method adopted, most valuers using traditional methods favoured the use of an all‐risk/equivalent yield approach using layered income flows. Those using DCF preferred the short‐cut approach with a reversion to a sale price after the first change of income. Clearly suggests that there is a greater understanding of explicit techniques than anecdotal evidence had previously suggested.

Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers.

Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry.

On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing.

Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.

Wars can lead to massive migration waves, e.g. the Syrian Civil War that began in 2011 led to the European refugee crisis. Discussions about labour integration often accompany huge spikes in asylum applications. Since many refugees experience traumatic events, such as torture, health problems must be considered in the work context. Unfortunately, the relevance of the post-traumatic experiences of refugees to specific job performance facets has mainly been ignored. To close the research gap, a study with refugees living in Germany was conducted to examine to what extent trauma-related variables are relevant to performance variables.

The relationship between post-traumatic stress (PTS), post-traumatic growth (PTG) and adaptive performance (social and task-oriented adaptive performance) was examined as, nowadays, many activities require dealing with changes (e.g. new colleagues, new technologies). Sixty-nine people met the inclusion criteria (e.g. refugee status, traumatic experiences) and completed the survey. Data were analysed using the Bayesian approach.

Bayesian analyses revealed that PTG might promote both social and task-oriented adaptive performance. In contrast, PTS was negatively related to task-oriented adaptive performance. Furthermore, there was no substantial evidence that PTG moderates the relationship between PTS and adaptive performance. Furthermore, there is preliminary evidence that refugees do not differ substantially from the general population with respect to social adaptive performance.

The present study demonstrates for the first time the relevance of post-traumatic experiences to the adaptive performance of refugees.