Decision Tree for Initial Decisions

Fast Facts:

Decision tree charts give unique data that might not be available from other charts, such as a breakout of immediate decisions from other initial decisions. They also show the percentages of each decision type and the actual values.

Origin Reports’ Chart of the Month – July:

Decision Tree for Initial Decisions

 

July 14, 2021

By: Sherrie Hill & Jason Roberts

Decision trees provide an instant visual summary of the outcomes of your peer review process, offering details that may otherwise get buried when data are presented in a tabular form. Decision trees offer an overview of a process and details of that process all at once.  They can be displayed either vertically or horizontally.  The figure above is an example of a vertical decision tree that outlines the initial decision ratios for a journal.  When discussing a vertical decision tree, you should move from the top to the bottom and discuss the elements of each row in comparison.

In this example, the blue box shows the number of manuscripts that received a decision during the set time frame, in this case the year 2019, as shown in the chart’s Parameters section.

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The next row separates the manuscripts that received immediate decisions from the manuscripts that went through peer review. Immediate decisions are decisions that are rendered by Editors without any input from peer reviewers.

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When creating an initial decision tree, you must decide how you want to handle manuscripts that do not have an actual “immediate reject” or “immediate accept” decision but did not go through peer review. This might occur because your journal does not have an immediate reject or immediate accept decision type or because the manuscript was assigned to an associate editor/section editor and rejected by that editor without comments from peer reviewers. In Origin Reports, the user is given the option of deciding how to categorize these types of manuscripts by answering a series of questions before generating the chart.

In this example that shows 159 manuscripts received an initial decision in 2019, the journal rendered 44 immediate rejections and no immediate accept decisions, with the remaining 115 manuscripts being sent through peer review.

It is helpful to understand the percentage of manuscripts that end up in each of these categories. These percentages help a journal determine editorial staffing needs. If there is a very high percentage of immediate decisions, this may indicate that your journal has very high standards for manuscripts that are sent to peer reviewers. This closer scrutiny takes more time and can indicate that your journal needs a larger triage team to ensure a bottleneck does not emerge at this stage. A large percentage of manuscripts being sent for full peer review, may indicate that you need to continually monitor the number of editors handling these manuscripts, to ensure you have enough people to move manuscripts seamlessly through to peer review process.

The final row of boxes in this decision tree shows how manuscripts sent for full peer review fared.  In our example, another 35 (30%) of the manuscripts were rejected after peer review, while the remaining 80 manuscripts were either accepted or received some type of a revision decision.  A very high percentage of manuscripts rejected following peer review may indicate that your journal could benefit from a more rigorous initial triage process prior to assigning the manuscripts to editors. Removing clearly unpublishable or unwanted manuscripts prior to editor assignment and peer review protects these resources from being overburdened.

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Though the Immediate Reject box and the Peer Review box show the rejection rate at various stages, it is good to show an overall rejection rate for your journal. This can be calculated by adding together the number of manuscripts rejected at each stage and dividing it by the total number of manuscripts that received a decision (number of rejects/number of rejects + number of accepts + number of revise) in that time period (multiplied by 100 to form a percentage). This is usually a final bit of information that your editorial staff will want to know, since the rejection rate is typically reported as a single value.

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Decision trees are easy to interpret and a fast way to absorb complex information, such as the breakdown of initial decisions. They can also be helpful at giving you a better understanding of how your processes are running and provide signposts to where initial assessment criteria or staffing levels require adjustment.

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