Put your efforts into creating an efficient workflow through effective discipline and you’ll find success.
A key goal for publishers of scientific and academic journals is to facilitate approval of an author’s work with as little struggle as possible – to create a process that includes enough checkpoints and procedures to minimize proofing-cycle changes and ensure approval the first time. Although technology is a useful tool, boosting first-proof acceptance rates is primarily a matter of building a disciplined workflow. With that in mind, let’s look at four of the most effective tactics for getting there.
1. Start with good content
The technology maxim “garbage in, garbage out” also applies to workflow. No matter how airtight your process might be, if an author’s data is flawed or incomplete, or if their figures and images aren’t properly rendered per your specifications, your workflow will necessarily slow down or even stop while changes and corrections are implemented.
The key is to make sure that the data authors are using to support their work is complete and accurate, that they’ve done their due diligence, are confident in the veracity of the research, and that any images or figures they’re using have been properly formatted prior to submission. Similarly, if the authors are responsible for editing their work prior to submission, they need to take responsibility for the quality of the work.
2. Implement inventory checks and automation
It’s also useful to build inventory checks into each stage of the process to help ensure that only the correct components get pushed on to the next workflow point. Checking the data as it moves through the system ensures that errors aren’t introduced unexpectedly, which becomes more likely when multiple hands are working on the material during editorial review and revision.
Automating more elements of your workflow allows you to more tightly control the process of converting content from one form to another while minimizing human error.
Likewise, automating more elements of your workflow allows you to more tightly control the process of converting content from one form to another while minimizing human error. Ideally, once your author is past the point of evaluating a proof, there should be a very low incidence of new requested content changes prior to publication.
3. Time your QA checks appropriately
Even as automation can make your process more machine-like, there’s no substitute for a sharp set of human eyes reviewing things at key points in the workflow. It’s at these crucial points, not at the end of the process, where the quality assessment (QA) must be performed.
Placing QA reviews at the back-end of your workflow simply creates bottlenecks, particularly in those situations where the material is relatively complex. For example, you might have text in a foreign language that reads right to left juxtaposed with a translation that reads left to right. Such things need to be vetted through the QA process well before pagination; otherwise, your machine-like process could grind to a halt while issues are corrected. The same holds true for objects inside the piece, such as large complex tables or mathematical notations. In such cases, you’ll want to conduct QA checks after each transformation to ensure that everything has come through the process correctly.
There’s no substitute for a sharp set of human eyes reviewing things at key points in the workflow.
Though some QA checks can be automated, others will still require a human set of eyes for confirmation. For example, your system might be able to automatically convert an equation into proper MathML, but you’ll still need someone to look at the result to ensure that the equation is represented in the logical manner intended by the author. You’ll also need to make sure that any explanatory notes associated with the equation appear in the appropriate place relative to the equation itself. This is important because sometimes meaning or intent is made clearer simply based on how objects (like equations and notes, for example) are positioned relative to one another.
4. Help authors proof their work
When it’s time to proof a piece, either before the copyediting phase or after (or both), it’s critical to make sure that authors receive clear, concise guidelines about how to go about doing so. In some cases, when guidelines are not clear, authors can come to believe that they’re being asked to edit or revise their work in process. They might think you’re looking for feedback or notes about altering the content, when in fact they simply need to evaluate it for any errors, ensure that it accurately represents what they submitted, and that the meaning and intention of the work are clear.
You’ll also need to make sure that any time you receive a submission with multiple contributors, you assign a single author to act as the point person for approvals and feedback. This author will be charged with collecting information from the other contributors – an absolute necessity, as attempting to manage the input of five, 10, or even 50 authors simply isn’t reasonable.
Implementing a time limit on proof approvals is another good tactic for working more efficiently with authors. Giving them a specific deadline – 48 hours, for example – by which to provide feedback or risk triggering an automatic acceptance of the existing proof encourages a speedy turnaround, keeps your workflow on track, and helps ensure the highest quality of work in the initial submission.
As we’ve seen, creating a workflow that improves first-proof approval rates doesn’t require large sums of money. In most every case, exerting common sense, discipline, and attention to detail can make a big difference. Put your efforts into creating an efficient workflow through effective discipline and you’ll find success regardless of the tools and technology at your disposal.
For a discussion on how these tactics might apply to your own workflow, reach out.