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Grey Literature for Health Sciences

Assessment Tools

The AACODS checklist is a checklist designed for the evaluation and critical appraisal of grey literature. It covers the following areas:

  • Authority: who is responsible for the content?
  • Accuracy: is the content clear and consistent?
  • Coverage: what is the scope?
  • Objectivity: what are the underlying biases (stated or unstated)?
  • Date: how current is the content?
  • Significance: is the resource meaningful, representative, or impactful?

DARTS is another tool that can be used to evaluate grey literature, and can easily be adapted into a spreadsheet for tracking purposes.

  • Date: when was the content last updated?
  • Author: who created the content?
  • References: are there valid references to other content?
  • Type: what is the purpose of the content? Where is it featured?
  • Sponsor: is the content sponsored, and by whom?

QUality Evaluation Scoring Tool (QUEST) is a 28-point system for evaluating online health information, and can be useful for comparing a large number of resources.

  • Authorship: 0 (no indication), 1 (some indication), 2 (author's name and qualification listed)
  • Attribution: 0 (no references), 1 (some references; may not be credible studies), 2 (reference to at least one identifiable scientific study), 3 (references to identifiable scientific studies in >50% of claims)
  • Conflict of interest: 0 (endorsement of related intervention or treatment), 1 (endorsement of educational products/services), 2 (unbiased)
  • Currency: 0 (no date present), 1 (dated 5 years or older), 2 (dated within 5 years)
  • Complementarity: 0 (no support of patient-physician relationship), 1 (support of patient-physician relationship)
  • Tone: 0 (author fully and unequivocally supports claims, using strong language), 1 (author mainly supports claims with more cautious language, but does not discuss limitations), 2 (author's claims are cautious and balanced, and discusses limitations/contrasting findings)

Tips

  • Keep a spreadsheet for data management and evaluation, as grey literature resources tend to lack built-in data management tools.
  • Match study protocols and clinical trials with published studies when possible. This will make a systematic review more comprehensive, and also help identify potential biases in outcome or analysis reporting in the studies. For more information on how, see this methods guide.
  • Try to contact the author(s) when more information on a study’s eligibility, design, or risk of bias is needed.