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The 'snowball effect': short and long-term consequences of early career alcohol industry research funding.

Gemma MitchellJim McCambridge
Published in: Addiction research & theory (2021)
Despite extensive evidence of bias resulting from industry sponsorship of research across health sciences, and longstanding concerns about alcohol industry research funding, there has not been a strong tradition of empirical research on this subject. This study explores researcher decision-making regarding industry funding at the early career stage and the consequences of such funding. Data were derived from semi-structured interviews with researchers working on alcohol policy-relevant topics who first received alcohol industry funding early in their careers ( n  = 7). Data were analyzed thematically using NVivo software. These early-career researchers largely initiated contact with the industry by applying for funding, mostly from industry research funding organizations. Their decisions were shaped by their research environments, where seeking alcohol industry funding early in careers was normative, in large part due to senior colleagues and peers having connections to the industry. Despite being 'no strings attached' a 'snowball' effect occurred, whereby initial funding led to more industry funding and other opportunities. Receiving early career industry funding had long-term consequences for researchers, not only shaping research networks but also leading to reputational harms as norms around the acceptability of industry funding changed. Exploring this controversial subject in the context of researcher careers adds depth and meaning to larger quantitative studies on bias resulting from industry sponsorship, and identifies mechanisms through which bias may be produced. Further research is required to study the impact of these processes on alcohol policy-relevant research agendas, and also to explore the wider generalizability of these exploratory findings.
Keyphrases
  • healthcare
  • public health
  • mental health
  • decision making
  • gene expression
  • medical students
  • electronic health record
  • mass spectrometry
  • machine learning
  • palliative care
  • social media
  • data analysis