Team adaptability and task cohesion as resources to the non-linear dynamics of workload and sickness absenteeism in firefighter teams

ABSTRACT

The current study builds on the non-linear Dynamic Systems (NDS) perspective to test the assumption that change in sickness absenteeism is non-linear, and that such change is due to workload, team adaptability and task cohesion. Participants were 37 firefighter teams (n = 250 individuals) from a main European capital city. The research hypotheses were tested using SPSS and the “cusp” package, in the statistical software R. The results suggest that change in sickness absenteeism behaviours over time is non-linear, with the cusp catastrophe model predicting such behaviours better than the linear and logistic models. In our model, task cohesion functions as an asymmetry factor (i.e., the independent variable that determines the strength and discrepancy between the two stable states of the dependent variable) leading to a linear change in sickness absenteeism. Interestingly, both workload and team adaptability function as bifurcation (i.e., the independent variable that determines the change between the two stable states of the order parameter) and asymmetry factors leading to non-linear and linear change in sickness absenteeism over time. This study contributes to the growing evidence that incorporating the NDS perspective enables a better understanding of action teams, namely those working in extreme environments.     

The dynamic mechanism of a novel stochastic neural firing pattern observed in a real biological system

In the experimental neural pacemaker of a rat, a novel firing pattern has been discovered. This pattern was generated between the period 2 firing pattern and the period 3 firing pattern during the periodic adding bifurcation and inverse periodic adding bifurcation. The pattern was observed and analyzed in the present investigation. The composition of this novel firing pattern could be regarded as a transition between a string of period 2 burst and a string of period 3 burst without single period 2 or period 3 burst, which was different from those chaotic and stochastic neural firing patterns in previous reports. It was identified to be stochastic by the inter-event intervals (IEIs) analysis, although it exhibited chaos-like characteristics with the results of the inter-spike intervals (ISIs) analysis. The numerical simulation suggested that the new pattern observed in the real biological system could be simulated in the stochastic Chay model but not in the deterministic model. With the signal to noise ratio (SNR) analysis and bifurcation analysis, this novel firing pattern was considered to be generated by stochastic resonance under the influence of noise near the periodic adding (inverse) bifurcation point. The probability analysis of transformed binary chain further confirmed that the origin of stochastic and chaos (deterministic)-like characteristics of this novel firing pattern.