Chemotherapy is often administered in openly designed hospital wards, where the possibility of patient–patient social influence on health exists. Previous research found that social relationships influence cancer patient’s health; however, we have yet to understand social influence among patients receiving chemotherapy in the hospital. We investigate the influence of co-presence in a chemotherapy ward. We use data on 4,691 cancer patients undergoing chemotherapy in Oxfordshire, United Kingdom who average 59.8 years of age, and 44% are Male. We construct a network of patients where edges exist when patients are co-present in the ward, weighted by both patients’ time in the ward. Social influence is based on total weighted co-presence with focal patients’ immediate neighbors, considering neighbors’ 5-year mortality. Generalized estimating equations evaluated the effect of neighbors’ 5-year mortality on focal patient’s 5-year mortality. Each 1,000-unit increase in weighted co-presence with a patient who dies within 5 years increases a patient’s mortality odds by 42% (β = 0.357, CI:0.204,0.510). Each 1,000-unit increase in co-presence with a patient surviving 5 years reduces a patient’s odds of dying by 30% (β =−0.344, CI:−0.538,0.149). Our results suggest that social influence occurs in chemotherapy wards, and thus may need to be considered in chemotherapy delivery.
Social influence on 5-year survival in a longitudinal chemotherapy ward co-presence network
JEFFREY LIENERT, CHRISTOPHER STEVEN MARCUM, JOHN FINNEY, FELIX REED-TSOCHAS
More than 300 years ago, the philosopher René Descartes asked a disturbing question: If our senses can’t always be trusted, how can we separate illusion from reality? We’re able to do so, a new study suggests, because our brain keeps tabs on reality by constantly questioning its own past expectations and beliefs. Hallucinations occur when this internal fact-checking fails, a finding that could point toward better treatments for schizophrenia and other psychiatric disorders.
Understanding how humans sustain cooperation in large, anonymous societies remains a central question of both theoretical and practical importance. In the laboratory, experimental behavioural research using tools like public goods games suggests that cooperation can be sustained by institutional punishment—analogous to governments, police forces and other institutions that sanction free-riders on behalf of individuals in large societies1,2,3. In the real world, however, corruption can undermine the effectiveness of these institutions4,5,6,7,8. Levels of corruption correlate with institutional, economic and cultural factors, but the causal directions of these relationships are difficult to determine5,6,8,9,10. Here, we experimentally model corruption by introducing the possibility of bribery. We investigate the effect of structural factors (a leader’s punitive power and economic potential), anti-corruption strategies (transparency and leader investment in the public good) and cultural background. The results reveal that (1) corruption possibilities cause a large (25%) decrease in public good provisioning, (2) empowering leaders decreases cooperative contributions (in direct opposition to typical institutional punishment results), (3) growing up in a more corrupt society predicts more acceptance of bribes and (4) anti-corruption strategies are effective under some conditions, but can further decrease public good provisioning when leaders are weak and the economic potential is poor. These results suggest that a more nuanced approach to corruption is needed and that proposed panaceas, such as transparency, may actually be harmful in some contexts.
Corrupting cooperation and how anti-corruption strategies may backfire
Michael Muthukrishna, Patrick Francois, Shayan Pourahmadi & Joseph Henrich
Nature Human Behaviour 1, Article number: 0138 (2017)
Functional connectivity (FC) analysis has revealed stable and reproducible features of brain network organization, as well as their variations across individuals. Here, we localize network markers of individual variability in FC and track their dynamical expression across time. First, we determine the minimal set of network components required to identify individual subjects. Among specific resting-state networks, we find that the FC pattern of the frontoparietal network allows for the most reliable identification of individuals. Looking across the whole brain, an optimization approach designed to identify a minimal node set converges on distributed portions of the frontoparietal system. Second, we track the expression of these network markers across time. We find that the FC fingerprint is most clearly expressed at times when FC patterns exhibit low modularity. In summary, our study reveals distributed network markers of individual variability that are localized in both space and time.
Spatiotemporal Network Markers of Individual Variability in the Human Functional Connectome
Cleofé Peña-Gómez Andrea Avena-Koenigsberger Jorge Sepulcre Olaf Sporns
Cerebral Cortex, https://doi.org/10.1093/cercor/bhx170