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Individual Placement and Support (IPS) is an evidence-based employment model, effective in supporting individuals with severe mental health difficulties to gain competitive employment. Irish mental health policy recognises its value and IPS is being rolled out in a national programme. Employment is recognised an important contributor to mental health recovery and social inclusion. However, research on IPS has tended to focus on competitive job outcomes. The purpose of this study was to explore the non-vocational outcomes of IPS in an Irish context.

A qualitative research approach was used to interview participants taking part in IPS within community mental health teams. Twelve interviews were included in the data analysis process which was informed by a thematic analysis approach.

Participants experienced increased confidence and positivity, both within a work context and whilst job seeking. More purposeful time use, participation in activities and engagement with society were also experienced by those employed and those at the job search stage of IPS.

This study contributes to the literature about the non-vocational benefits of IPS within an Irish context, highlights the mental health recovery benefits of taking part in IPS and supports the need for ongoing development of IPS throughout mental health services in Ireland.

The purpose of this study is to determine whether the fine wine market is efficient between homogeneous lots and heterogeneous lots.

Auction price data for homogeneous (or solid) lots of fine wines was analyzed to create price prediction models. Those models were used to predict the expected auction price for the bottles within heterogeneous lots. Lastly, models were created to explain and predict the differences between expected and realized prices for heterogenous wine lots.

The results show that large inefficiencies exist. The more complex and expensive the heterogeneous lot, the greater the discount relative to what would have been realized if the bottles had been sold individually. This discount can exceed 50% of the expected auction price.

Heterogeneous lots may arise as a practical requirement from the auction house. Restaurant buyers probably have little interest in such lots because of the inclusion of wines the restaurant will be unable to sell. Collectors may be uniquely positioned to benefit from this price discount.

These results are unique in the literature, because the price dynamics of heterogeneous (or mixed) lots of fine wines have not previously been studied.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

This paper investigates the factors and how they lead to meat choice decisions based on preferred slaughter practices. The literature has established the role of psychological factors and morality perception in meat choice decisions. However, it explores how consumers' behavioural intention is impacted towards alternative meat when consumer guilt is activated in different cultural settings.

This study included in-depth interviews with consumers from India's emerging market due to its multicultural dimension and diverse religious beliefs about meat consumption. The authors conducted 17 interviews to explore antecedents towards non-halal meat choices.

Utilizing the Theory of planned behaviour (TPB), this paper explores research gaps related to meat consumption preferences based on preferred slaughter practices in an emerging market context. The findings uncover and add to understanding meat preferences in varied cultural contexts that affect consumer choices. The authors advance the current understanding of TPB from the perspective of behavioural intention toward non-halal meat.

The study's findings have significant implications for all the organizations/outlets dealing with non-vegetarian food products, whether packaged or fresh and for meat sellers.

The study is unique in identifying the meat choice preferences based on slaughter practice through the extended prism of TPB. The market chosen for this study is one of the biggest consumer markets and its growing continuously.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

The present study aimed at developing and validating a self-development training package and determining self-development's effectiveness on job variables and human capital agility among secondary school principals in Isfahan.

In the first phase, the researcher conceived a full version of the self-development training package by studying the theoretical evidence of research and interviewing experts as well as using content analysis. The questionnaire was presented to several experts (n = 8) in that field of study for evaluation after compiling the initial version of the self-development training package, with the aim of determining the face and content validity. In the second phase of the study, the effectiveness of the self-development training package was experimented on secondary school principals using a quasi-experimental pre-test–post-test design and follow-up with a control group.

The evaluation results were suitable for the training package based on the proposed Lawshe method. The results also indicated that the implementation of a self-development training package in the experimental group had a significant effect on job performance and its dimensions, as well as human capital agility. The effect of the training package on increasing job performance in the post-test stage was 55.3% and was 50.2% in the follow-up stage. Also, the effect of this package on increasing the agility of human capital in the post-test phase was equal to 34.8% and was equal to 28.9% in the follow-up stage.

Question 1: What are the components of a self-development training package? Question 2: What is the credibility of the developed training package from the experts' point of view? Question 3: Does the self-development training package have an effect on job performance and its dimensions? Question 4: Does the self-development training package have an effect on human capital agility?.

The purpose of this paper is to evaluate the impact of leadership development programmes, underpinned by Transformational Learning Theory (TLT).

A corpus-informed analysis was conducted using survey data from 690 participants. Data were collected from participants’ responses to the question “please tell us about the impact of your overall experience”, which culminated in a combined corpus of 75,053 words.

Findings identified patterns of language clustered around the following frequently used word types, namely, confidence; influence; self-awareness; insight; and impact.

This in-depth qualitative evaluation of participants’ feedback has provided insight into how TLT can be applied to develop future health-care leaders. The extent to which learning has had a transformational impact at the individual level, in relation to their perceived ability to influence, holds promise for the wider impact of this group in relation to policy, practice and the promotion of clinical excellence in the future. However, the latter can only be ascertained by undertaking further realist evaluation and longitudinal study to understand the mechanisms by which transformational learning occurs and is successfully translated to influence in practice.

Previous research has expounded traditional leadership theories to guide the practice of health-care leadership development. The paper goes some way to demonstrate the impact of using the principles of TLT within health-care leadership development programmes. The approach taken by The Florence Nightingale Foundation has the potential to generate confident leaders who may be instrumental in creating positive changes across various clinical environments.