Student nurses' views on shift patterns: what do they prefer and why? Results from a Tweetchat

Aim: To understand student nurses' views around shift patterns. Design: Qualitative study Method: We held a Tweetchat in May 2019, where we asked questions around the frequency of 12-hour shifts working on placement; schedule flexibility while on placement; which shift patterns they preferred and why. Data from the Tweetchat were analysed to identify emerging themes and inductively develop a coding frame. Results: Seventy-three nursing students participated in the Tweetchat. The majority (68%) of respondents reported that they work 12-hour shifts on placements. We identified three main overarching themes: "Achieving a personal equilibrium"; "Meeting the needs of the care environment", "Factors affecting negotiation capacity". Data highlighted a conflict for most students, where they preferred 12-hour shifts because of more time off for study, paid work, and leisure, while acknowledging 12-hour shifts had a negative effect on fatigue, exhaustion, and their ability to achieve self-care (i.e. poor diet, no exercise).

Expectations-Based Reference-Dependence and Choice Under Risk

This article characterises the behavioural content of a model of choice under risk with reference-dependent preferences and endogenous expectations-based reference points based on the preferred personal equilibrium model of Kőszegi and Rabin (2006). The combination of reference-dependent preferences and endogenous reference points leads to violations of the Independence Axiom and can also lead to violations of the Weak Axiom of Revealed Preference. An axiomatic characterisation shows that the model places testable restrictions on choice under risk.

Bayesian Networks and Boundedly Rational Expectations *

I present a framework for analyzing decision making under imperfect understanding of correlation structures and causal relations. A decision maker (DM) faces an objective long-run probability distribution p over several variables (including the action taken by previous DMs). The DM is characterized by a subjective causal model, represented by a directed acyclic graph over the set of variable labels. The DM attempts to fit this model to p , resulting in a subjective belief that distorts p by factorizing it according to the graph via the standard Bayesian network formula. As a result of this belief distortion, the DM’s evaluation of actions can vary with their long-run frequencies. Accordingly, I define a ”personal equilibrium” notion of individual behavior. The framework enables simple graphical representations of causal-attribution errors (such as coarseness or reverse causation), and provides tools for checking rationality properties of the DM’s behavior. I demonstrate the framework’s scope of applications with examples covering diverse areas, from demand for education to public policy.