Navigating Nurses: Supporting Retention Through Mentorship.

Retaining nurses is critical. Doing so effectively requires identifying not only why some nurses are leaving but also why others are staying. Discovering a sense of belonging and opportunities for professional development, especially for early career nurses, are two common themes. A mentorship framework was created within a nurse residency program integrating both motives, with positive results for the mentors and mentees alike. [J Contin Educ Nurs. 2023;54(9):389-391.].

Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts.

OBJECTIVE: This study manipulates the presence and reliability of AI recommendations for risky decisions to measure the effect on task performance, behavioral consequences of trust, and deviation from a probability matching collaborative decision-making model. BACKGROUND: Although AI decision support improves performance, people tend to underutilize AI recommendations, particularly when outcomes are uncertain. As AI reliability increases, task performance improves, largely due to higher rates of compliance (following action recommendations) and reliance (following no-action recommendations). METHODS: In a between-subject design, participants were assigned to a high reliability AI, low reliability AI, or a control condition. Participants decided whether to bet that their team would win in a series of basketball games tying compensation to performance. We evaluated task performance (in accuracy and signal detection terms) and the behavioral consequences of trust (via compliance and reliance). RESULTS: AI recommendations improved task performance, had limited impact on risk-taking behavior, and were under-valued by participants. Accuracy, sensitivity (d'), and reliance increased in the high reliability AI condition, but there was no effect on response bias (c) or compliance. Participant behavior was only consistent with a probability matching model for compliance in the low reliability condition. CONCLUSION: In a pay-off structure that incentivized risk-taking, the primary value of the AI recommendations was in determining when to perform no action (i.e., pass on bets). APPLICATION: In risky contexts, designers need to consider whether action or no-action recommendations will be more influential to design appropriate interventions.

Observing the self, avoiding the experience? The role of the observer perspective in autobiographical recall and its relationship to depression in adolescence.

The tendency to adopt an observer perspective (OP) when recalling autobiographical memories has been shown to be related to both avoidance and depression in adults. Very little research has examined this relationship in adolescents, however, and none of this work has adopted a longitudinal paradigm. This is an important gap in light of the marked escalation in rates of depression across the adolescent period. The current study therefore examined the concurrent and longitudinal (one year) relationships between observer perspective in the Minimal Instruction Autobiographical Memory Test (Mi-AMT; Debeer, E., Hermans, D., & Raes, F. (2009). Associations between components of rumination and autobiographical memory specificity as measured by a minimal instructions autobiographical memory test. Memory, 17(8), 892-903. https://doi.org/10.1080/09658210903376243), avoidance, and depression in a large, longitudinal sample of adolescents (mean age = 15.03 at T1). Consistent with predictions we found a significant but small cross-sectional correlation between OP and higher levels of depressive symptoms, however the relationship with avoidance was not significant. Contrary to predictions, the longitudinal relationships of OP with avoidance and depression were not significant. These findings raise the possibility that OP may be negligibly related to avoidance or depression during adolescence.

How robust is familiar face recognition? A repeat detection study of more than 1000 faces.

Recent theories suggest that familiar faces have a robust representation in memory because they have been encountered over a wide variety of contexts and image changes (e.g. lighting, viewpoint and expression). By contrast, unfamiliar faces are encountered only once, and so they do not benefit from such richness of experience and are represented based on image-specific details. In this registered report, we used a repeat detection task to test whether familiar faces are recognized better than unfamiliar faces across image changes. Participants viewed a stream of more than 1000 celebrity face images for 0.5 s each, any of which might be repeated at a later point and has to be detected. Some participants saw the same image at repeats, while others saw a different image of the same face. A post-experimental familiarity check allowed us to determine which celebrities were and were not familiar to each participant. We had three predictions: (i) detection would be better for familiar than unfamiliar faces, (ii) detection would be better across same rather than different images, and (iii) detection of familiar faces would be comparable across same and different images, but detection of unfamiliar faces would be poorer across different images. We obtained support for the first two predictions but not the last. Instead, we found that repeat detection of faces, regardless of familiarity, was poorer across different images. Our study suggests that the robustness of familiar face recognition may have limits, and that under some conditions, familiar face recognition can be just as influenced by image changes as unfamiliar face recognition.

Who are the lonely? A typology of loneliness in New Zealand.

BACKGROUND: Loneliness has many negative physical and mental health ramifications and is most prevalent among vulnerable social groups. However, little is known about how loneliness is grouped within the population and the characteristics of those groups. METHODS: We conducted a Latent Profile Analysis on 18,264 participants from the fifth wave of the New Zealand Attitudes and Values Study to identify the number of distinct loneliness profiles in the population. Secondary analyses then determined the representation of various demographic and psychosocial characteristics of participants within each profile. RESULTS: Analyses identified four distinct loneliness profiles: 'high-loneliness' (5.7%), 'low-loneliness' (57.9%), 'appreciated outsiders' (29.1%; who received acceptance from others but felt like social outsiders) and 'superficially connected' (7.2%; who showed the opposite pattern). 'High-loneliness' were the most introverted, emotionally unstable and poorest in wellbeing. 'Appreciated outsiders' and 'superficially connected' had moderate wellbeing, but 'appreciated outsiders' were relatively higher in wellbeing despite greater introversion and neuroticism. CONCLUSION: This research provides a typology of loneliness in New Zealand and identifies groups more likely to experience loneliness. The 'appreciated outsiders' and 'superficially connected' profiles provide fresh insight into how loneliness may manifest and the relative influences of quality and quantity of social contacts on wellbeing.