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.     

An introduction to the Priority‐Pointing Procedure

A qualitative research‐based diagnostic procedure, the Priority‐Pointing Procedure (PPP), is proposed that synthesizes responses to open‐ended questions about the direction an organization should take. It points to a priority for action by measuring imbalances in the numbers of responses in the context of the structure of adjustment decision‐making from nomology. It is shown to be useful for defining variables and structuring criteria for strategic Multi‐Criteria Decision Analysis (MCDA) problems. Copyright © 2000 John Wiley & Sons, Ltd.     

An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use.