Happiness and co-creation of value: Playing the blues

Little has been written about co-creational aspects of happiness. Happiness is generally treated in the marketing literature as an individual outcome of exchange. However, the notion of value in exchange has been challenged by service-dominant (S-D) logic. To stimulate the research discovery process, an account of co-creation of happiness is offered, based on the experience of the lead author, in playing blues music. We propose value is co-created in a context when it is perceived by an individual to be adding to their happiness/subjective well-being (SWB). Thus, the concepts of value and happiness/SWB are closely linked and interconnected. The contribution to S-D logic is in recognising the interconnectedness between value co-creation and happiness/SWB. In particular, this article draws attention to the co-creative role of the artist, in cultural ecosystems. This is relevant to the development of the field and has potentially significant implications for policy in allocating society’s resources.

Are Patient Experience and Outcomes for Home Health Agencies Related?

To facilitate home health agency (HHA) selection, CMS released patient experience star ratings on the Home Health Compare website in January 2016. Our objective was to understand the relationship between patient experience and outcomes in HHAs. We utilized publicly reported data to evaluate the relationships among patient experience star ratings, summary quality of care star ratings (comprised primarily of outcome measures), and individual outcome measures for 4,249 HHAs. Results indicate a weak correlation between patient experience and quality stars ( r = .13, p < .001). The difference between the lowest and highest rated HHAs for patient experience is associated with only a half-star improvement in quality stars. The associations between patient experience and individual outcome measures varied, with functional outcomes most strongly associated with patient experience. Findings highlight the importance of reporting separate quality domains; however, conflicting ratings may complicate the HHA selection process and introduce misaligned incentives for HHAs.

A REVIEW OF PERSON-ORGANIZATIONAL CONGRUENCE AND OUTCOME VARIABLES

The accessible writing is occupied with affirmation proposing the necessity for picking and utilizing proper individuals for the organization's, likewise as reporting the significance of congruence among people at workplaces with the organization and making this relationship among the duo stronger for the general accomplishment of the great number of objectives of the organization and all the stakeholders associated directly or indirectly with the organizations. Likewise, researchers, experts and practitioners in the field of organizational behaviour and its allied fields across the globe have demonstrated and proved a lot of enthusiasm in examining the domain of person-organizational congruence in relation to several individual level organizational level outcomes like, organizational commitment, work satisfaction, organizational citizenship behaviour, work performance, turnover intention and intention to stay. Research’s that have been carried out as of now on person-organizational congruence and its relationship with several individual level and organizational level outcomes were limited in context to the Indian settings and growing number of studies advised a need to further investigate person-organizational congruence and its relationship with individual and organizational level outcomes. In light of this developing essentialness associated to the phenomenon of person-organizational congruence domain the present examination is as such a modest undertaking toward this way. KEY WORDS: person-organizational congruence, organizational outcome variables, individual outcome variables

Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort

Objective We aimed to investigate bias in applying machine learning to predict real-world individual treatment effects. Materials and Methods Using a virtual patient cohort, we simulated real-world healthcare data and applied random forest and gradient boosting classifiers to develop prediction models. Treatment effect was estimated as the difference between the predicted outcomes of a treatment and a control. We evaluated the impact of predictors (ie, treatment predictors [X1], confounders [X2], treatment effects modifiers [X3], and other outcome risk factors [X4]) with known effects on treatment and outcome using real-world data, and outcome imbalance on predicting individual outcome. Using counterfactuals, we evaluated percentage of patients with biased predicted individual treatment effects Results The X4 had relatively more impact on model performance than X2 and X3 did. No effects were observed from X1. Moderate-to-severe outcome imbalance had a significantly negative impact on model performance, particularly among subgroups in which an outcome occurred. Bias in predicting individual treatment effects was significant and persisted even when the models had a 100% accuracy in predicting health outcome. Discussion Inadequate inclusion of the X2, X3, and X4 and moderate-to-severe outcome imbalance may affect model performance in predicting individual outcome and subsequently bias in predicting individual treatment effects. Machine learning models with all features and high performance for predicting individual outcome still yielded biased individual treatment effects. Conclusions Direct application of machine learning might not adequately address bias in predicting individual treatment effects. Further method development is needed to advance machine learning to support individualized treatment selection.