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Prediction models to identify workers at risk of sick leave..:
Lisa C Bosman
;
Lyan Dijkstra
;
Catelijne I Joling
...
https://www.sjweh.fi/show_abstract.php?abstract_id=3703. , 2018
Link:
https://doi.org/10.5271/sjweh.3703
RT Journal T1
Prediction models to identify workers at risk of sick leave due to low-back pain in the Dutch construction industry
UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:5770302a51d94d569831e8998e1cbac8&Exemplar=1&LAN=DE A1 Lisa C Bosman A1 Lyan Dijkstra A1 Catelijne I Joling A1 Martijn W Heymans A1 Jos WR Twisk A1 Corné AM Roelen PB Nordic Association of Occupational Safety and Health (NOROSH) YR 2018 K1 risk assessment K1 absenteeism K1 pain K1 musculoskeletal disease K1 construction industry K1 prediction model K1 prognostic research K1 roc analysis K1 low-back pain K1 sick leave K1 construction K1 worker K1 Public aspects of medicine K1 RA1-1270 JF https://www.sjweh.fi/show_abstract.php?abstract_id=3703 LK http://dx.doi.org/https://doi.org/10.5271/sjweh.3703 DO https://doi.org/10.5271/sjweh.3703 SF ELIB - SuUB Bremen
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