Sa-KEY-7 - Psychosocial Factors At Work And Health - What Have We Learned From Mega-Studies

Saturday May 20   09:00 AM to 10:00 AM (1 hour)
O'Reilly Hall
Psychosocial factors at work and health - what have we learned from mega-studies?
Prof Mika Kivimäki
University College London
The production of scientific knowledge is susceptible to bias at every stage of the process, from what questions are asked by the investigator, to which method is chose to gather data, to which analyses are conducted (e.g., “P-hacking,” wherein the method of statistical analysis and the degrees of freedom are manipulated until they yield statistically significant results). Even after completion of a study, authors sometimes choose not to submit their work for publication because they are not satisfied with the results (i.e., the “file drawer” problem), or they encounter difficulties with getting results published because of reviewer or editorial bias (“publication bias”). The lack of standard measures and the availability of several alternative measures to assess the same risk factors further complicates the interpretation of results. A null finding is not necessarily seen to add to scientific knowledge as it may be interpreted as a ‘false negative’ due to the use of non-optimal measures, or non-optimal categorization of measures. A positive finding, on the other hand, may also be considered not to offer definite proof of an association if similar caveats apply. In genetics, many of these limitations were addressed by adopting the approach of pooling of data from multiple studies, an advance stipulated by the funders of these studies. This use of better-powered studies clearly accelerated scientific progress in genetics. This keynote uses findings from the Individual-participant Meta-analysis in Working Populations (IPD-Work) consortium to discuss whether pooling of data sets into mega studies could also boost progress in research on psychosocial factors at work and health.

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