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Choose Possibly relevant if you feel the records are about both factors influencing practice, AND electronic fetal heartrate monitoring.
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We have introduced a number of new features and functions in Cochrane Crowd based on contributor feedback. These include enabling contributors to compete with another Cochrane Crowd contributor (in a fun and friendly way!).
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You can now also set your own screening targets. This can be done within the task preferences for a task. If you would like, we can periodically send you reminders about your targets
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| Of the correct. You can either go back and do more training or start screening Since you got less than 50% correct it's best if you redo the training before moving on. The pass mark for this course is 80%. You can have one more attempt at another set of records. | Of the correct. |
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Welcome to Cochrane Crowd. Before you get started on your Cochrane Crowd adventure it would be fantastic if you could take a minute to answer 5 brief questions. These questions help us to understand a little about who our crowd is. This in turn helps us to think about how we design the tasks as well as the guidance and training materials that support each task. We will not share the information you give here with other organisations.
How old are you? [Choose one]
Do you have any health-related research experience? [Choose one]
Do you know what a systematic review is? [Choose one]
How would you describe yourself [Tick all that apply to you]
What is the highest degree of level of education you have? [Choose one]
Cochrane Crowd (that’s you) are not only helping to identify studies, you are helping to advance our understanding of using crowdsourcing to speed up the production of health evidence. Listed below are some of the research outputs produced that have involved Cochrane Crowd.
An evaluation of Cochrane Crowd found that crowdsourcing produced accurate results in identifying randomized trials
https://pubmed.ncbi.nlm.nih.gov/33476769/
Cochrane Centralised Search Service showed high sensitivity identifying randomised controlled trials: A retrospective analysis
https://pubmed.ncbi.nlm.nih.gov/32798713/
Data reuse, machine learning, and crowdsourcing in Screen4Me
https://www.youtube.com/watch?v=WGeHo9dWS1k
Development of a search filter to identify reports of controlled clinical trials within CINAHL Plus
https://onlinelibrary.wiley.com/doi/full/10.1111/hir.12251
Evaluating Cochrane’s centralised search and screening processes: a retrospective analysis of the Cochrane Central Register of Controlled Trial’s (CENTRAL) coverage
https://www.youtube.com/watch?v=0I-Rmlml3tU&feature=youtu.be
Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975623
Living Systematic Reviews: 2. Combining human and machine effort
https://www.jclinepi.com/article/S0895-4356(17)30604-2/fulltext
Machine learning for identifying randomized controlled trials: an evaluation and practitioner’s guide
https://onlinelibrary.wiley.com/doi/full/10.1002/jrsm.1287
Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews
https://pubmed.ncbi.nlm.nih.gov/33171275/
Single-reviewer abstract screening missed 13 percent of relevant studies: a crowd-based, randomized controlled trial
https://www.sciencedirect.com/science/article/pii/S0895435619309825
State of the evidence: a survey of global disparities in clinical trials
https://pubmed.ncbi.nlm.nih.gov/33402333/
Training in the use of intrapartum electronic fetal monitoring with cardiotocography: systematic review and meta-analysis
https://doi.org/10.1111/1471-0528.16619
Working with a new kind of team: harnessing the wisdom of the crowd in trial identification
https://efsa.onlinelibrary.wiley.com/doi/full/10.2903/j.efsa.2019.e170715
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