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Work Assignment
Supervisors can assign, or configure MLM-AI to automatically assign abstracts for review to team members
When it is necessary to directly assign screening or QC activities to users, MLM-AI can operate in Work Assignment mode.
When Work Assignment is enabled:
- Supervisors can assign abstracts for screening or QC to other team members, and
- Users are only allowed to create decisions on abstracts directly assigned to them, either by a supervisor, or automatically by the system
- Work assignment feature is enabled via Settings. See Work assignment configuration page for mode details
- To make assignments, a supervisor user needs to be part of a team with the "Work assignment" permission enabled
Once work assignment is enabled, users will be able to see new tabs on the Review Details screen.
The amount of tabs visible to a user differs based on the their role in the system:
- Supervisors or users given the "Work Assignment" system privilege have access to all tabs, including Assigned, Unassigned and My Items
- Other users have access to the My Items tab only
Review Details view for a supervisor
Assignment tabs appear in green. They are organized as follows:
- Assigned (visible to team supervisors only) - it is a view of all assigned articles across the review. From this tab user is free to filter articles by Assignee (person assigned to an article), Assignment type and Work Status
- There are two types of work status: "Assigned Work To Do" means decision is not made by assignee yet, otherwise article has status "Assigned Work Done". Filtering by status is useful to track completed work or when looking for articles that could be re-assigned
Showing articles assigned for review from Assigned tab
- Unassigned (visible to team supervisors only) - all articles that don't have an assignee yet will appear in this tab
- My Items - everything that is assigned to current users is in this tab. The "Work Status" drop-down box helps identify work that waits to be done: when "Assigned Work To Do" is selected, it leaves articles without a decision or where decision is made by another user
Using "Work Status" filter from My Items tab
Use the Toggle Groups widget to reduce the number of visible tabs and customize the screen to your needs. can help achieving this. Once clicked, it will show a pop-up window providing a possibility to hide any group of tabs (Assignment tabs - green, Status tabs - dark blue, Miscellaneous tabs - brown).
Example on Toggling Tab Groups
To make an assignment, a supervisor needs to select one or more articles using check boxes on the left-hand side and click on the Assignments button:
Assignment from Review Detail
In the appeared pop-up window users can assign or unassign selected articles.
While assigning, a desired assignee and type of assignment have to be selected. On un-assignment, just a confirmation from a user is required to proceed:
Assignment/un-assignment process
The following rules apply on assignment conflicts:
- If user wants to assign an article that is already assigned, assignment will be overwritten
- If user wants to unassign an article without an assignment, nothing happens
A user with assignment privileges can make assignments from any tab of the review, including Assignment, Status and Miscellaneous groups.
Assignment status is visible from Article Detail view:
- Green icon - article is assigned to a current user
- Red icon - article is assigned to another user
- Grey icon - article is not assigned to anyone
Assignment states
It is possible for MLM-AI to assign work evenly and automatically, avoiding manual assignment of huge amounts of articles.
Auto-assignment is a monitor-level setting, it could be enabled or disabled from Create Monitor or Edit Monitor view. Abstracts are assigned to users configured with the "Abstract Review" role in round-robin fashion.
Auto-assignment scope
To enable auto-assignment for monitor, check "Auto-Assign Articles to Reviewer Teams":
Enabling auto-assignment on monitor
Once the monitor is saved, results for this monitor's review are going to be assigned to team members. To avoid favoring articles to certain users, an algorithm randomizes the list of article first, and assigns them to users in a round-robin way.
Last modified 2mo ago