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Our first aim was to detect clusters of different patterns amongst the 3-dimensional working hour characteristics time series ( ) t = 1 T where t index successive work shifts, w working hours, r rest-hours (recovery) after the work shift, and s work shift start time in hours x t, s ∈ [ 0,24 ) o’clock note, night shifts get implicitly represented by work shift start time and length). Unreliable short entries (all <3 hours) were removed according to previously defined procedures ( 8). Work shifts starting immediately after a previous one (with 1-minute precision) were considered to be part of the previous work shift (ie, they were removed after adding their hours to the previous shift). Therefore, the data contained the three work shift-related dimensions arranged along the dimension of work-shift succession (technically, a discrete 3-dimensional time series), and could contain up to 7845 scalar-valued observation values per employee. For each employee, we derived work shift lengths, between-shift rest periods (time after index shift and before the next shift), and shift starting times for all available successive shifts to capture the entirety of the employees’ recorded work time from 1 January 2008 to 27 August 2019 in a format conducive of time-series clustering. The records of working hours were drawn from Titania ® shift-scheduling program including the final realized working hours used for payroll. The main aims of the paper were to: (i) characterize working hour patterns in shift work by means of permutation distribution clustering as a data-mining tool, and (ii) study associations between these shift work patterns and sickness absence. In this study, we used data-mining tools to define working hour patterns in shift work over prolonged periods based on the following shift-specific parameters: work shift length, between-shift rest period, and shift starting time. More generally, the researcher- or hypothesis-derived pre-defined shift work patterns represent only a small subset of possible patterns, whereas modern data-mining tools would allow systematic exploration through a vastly larger space of possible patterns present in the given data. An earlier study found that long work shifts were associated with less sick leaves among hospital employees working irregular shifts raising speculations that the risk estimates for long shifts were confounded by other protective effects, such as longer recovery periods after the long shifts ( 12). By implication, quick returns (ie, short recovery after shift) are inversely associated with long shifts, which may contribute to the unexpected findings in studies that are focused on a single working hour characteristic only because both quick returns ( 11) and long working hours ( 3, 4) are associated with increased risk for negative health and well-being effects. Long work shifts, for example, are more common in hospitals during the nights and are often followed by longer time-off ( 9, 10). Wherein traditional statistical approaches often operationalize and test one confounding variable at a time, data-mining approaches can take in large quantities of data and automatically find patterns of confounding. This approach does not capture interrelations between working hour characteristics, which can have different effects on health, and it may also fail to capture relevant temporal aspects of the target working hour characteristic.
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10 am pdc to et full#
To date, most shift work research has been focused on one or few pre-defined shift work patterns rather than the full range of different shift work patterns.
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It remains uncertain which specific patterns of shift work are harmful as the concept of shift work captures a wide and heterogeneous set of working hour arrangements Working hour characteristics in shift work can also vary in terms of the length of the working hours (eg, the length of work shifts or work shift spells), shift intensity (defined by time between the individual shifts), and time of the day (timing of work shifts) ( 8). However, shift work captures a wide range of working hour arrangements, such as fixed night shift work rotating eight-hour shifts (bulk shift at a.m., p.m., and night) and irregular shift work characterized by a non-standard schedule with varying start and finish times, shift lengths, and rest periods between shifts ( 7). Shift work has been linked to increased risk of sickness absence (SA), occupational injuries, depression and various chronic conditions including the metabolic syndrome, type 2 diabetes mellitus, and coronary heart disease ( 2– 6).
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Shift work prevalence is 22% in the European working age population ( 1) and 20–25% in developed countries ( 2).