When Andre Kushniruk was diagnosed with advanced stage tongue cancer in the fall of 2017, his surgeon told him that the tumor was too large to operate. Kushniruk, a professor of Health Information Science at the University of Victoria in Canada, was given a slim chance of surviving past two years.

Kushiruk’s wife, also a health informatician, applied her skill and knowledge to verify his surgeon’s claim. Scouring through biomedical archives like PubMed, searching on Google and YouTube, and reading patient blogs, Kushniruk’s wife found that the situation wasn’t irreversible. Patients in cases similar to his had undergone surgery. Going through peer-reviewed literature, they identified a hospital and expert who could help Kushniruk. He underwent a life-changing surgery within a week.

Health information – the what and the why

This instance shows that patients and their family having access to health information can save lives. Kushniruk argues that his case also highlights the need for patients to know how and where to access the right information – something he and his wife could do because of their profession.

Health information is the knowledge that an individual, healthcare professionals and others need to safeguard their own health and the health of others. This includes the information needed for preventing diseases, maintaining health, disease screening, diagnosis and management as well as palliative care. Having access to health information can promote healthy habits, seeking appropriate healthcare, decision making, proper medication, and referral.

See also: The Role of Research Societies in Addressing Vaccine Hesitancy

Health information for all

Proposed by the British Medical Association, the World Medical Association (WMA) has unanimously approved a statement on healthcare information for all. The crux of the statement is that lack of access to timely, current, evidence-based healthcare information can contribute to morbidity and mortality, particularly in low- and middle-income countries. This campaign calls on doctors all over the world to support initiatives to improve access for health professionals, patients, and others.

This initiative, called Healthcare Information for All (HIFA), now has 20,000 members spread over 180 countries, interacting and discussing in several languages and working with the World Health Organization (WHO).

See also: Five Ways Research Societies Can Fight Fake News About Immunization

Social media as a source of health information

Social media served as the primary source of information during the COVID-19 pandemic. Social media has been used as a tool for health interventions, health campaigns, medical education, disease surveillance, and health promotion. Over 80% US state health departments have social media accounts.

Aside from US state health departments, several other research societies all over the world use social media to dispense information. Some of these societies and their work is highlighted below.

Australasian Society for Infectious Diseases

The Australasian Society for Infectious Diseases is a professional body for infectious diseases and microbiology in Australia. The society consists of infectious disease physicians, scientists, veterinarians, and other experts. They aim to advance education, promote research, and advocate for evidence-based policy matters in the field of infectious diseases. They hold events such as annual conferences where researchers in the field can present their work and collaborate. The society also gives awards to mentors, teachers, and researchers working in the field of microbiology and infectious diseases. In addition, they have resources in the form of articles and infographics for both clinicians and the public.

International Parkinson and Movement Disorder Society

The International Parkinson and Movenment Disorder Society brings clinicians, scientists, and other healthcare professionals together for collaborating on research and educating people about movement disorders. The regional sections of this society span all over the world – America, Europe, Africa, Asia, and Oceania.

They conduct events like international congresses which consist of lectures. They also educate others using unique formats like video challenges, where leading experts analyze cases live. The society also provides financial support and expert faculty for local educational activities in the field of movement disorders. Moreover, they celebrate international awareness days such as World Parkinson’s Day and World Movement Disorder Day. They bring together experts who amplify and boost knowledge about these conditions.

Research Society for the Study of Diabetes in India

Consisting of researchers and clinicians, the Research Society for the Study of Diabetes in India (RSSDI) aim to promote diabetes research. Aside from having their own scientific journal that publishes relevant research articles, RSSDI conducts several events to amplify diabetes research. These include walking campaigns to bring awareness to diabetes complications such as diabetic foot, and research retreats for scientists to present their work. They also provide research grants to researchers studying diabetes and release videos and infographics for patient awareness.

European Society of Clinical Microbiology and Infectious Diseases

The European Society of Clinical Microbiology and Infectious Diseases (ESCMID) started with the mission to improve people’s understanding of pathogens and infections. The non-profit organization, that now has over 9000 members, aims to improve the diagnosis, treatment, and prevention of infection-related diseases. They do this by promoting and supporting research, education, training, and good medical practices.

Some of the activities that the society engages in include conducting postgraduate courses, offering awards to researchers, and organizing summer schools. The organization also has their own scientific journal and a weekly newsletter to make health information easily accessible to the public.

American Cancer Society

The American Cancer Society is a cancer-fighting organization that aims to improve the lives of people with cancer and their families. They ensure that everyone has an opportunity to prevent, detect, treat, and survive cancers. They do this through their practices of advocacy, research, and patient support.

They have a 24/7 cancer helpline that provides support for people living with cancer. They also have patient and caregiver support programs that provide accurate health information for how to navigate when one gets a cancer diagnosis.

American Heart Association

The American Heart Association is USA’s oldest and largest voluntary organization dedicated to fighting heart disease and stroke. They bring together experts in the field and raise funding for cardiovascular research. They publish blog posts providing reliable information about various cardiovascular and related diseases. They partner with schools to improve nutrition, increase physical activity and help end tobacco use among youths in order to create healthier futures for them. They also train over 22 million people in cardiopulmonary resuscitation (CPR), an emergency medical procedure, each year.

Summary – the why and the how of healthcare information

Health information readily accessible to healthcare providers, patients, their family, and caregivers can possibly save lives. Several societies across the globe make sure that relevant and reliable information is available to the various stakeholders in order to help people living with health conditions.

The post How Research Societies are Making Health Information Accessible appeared first on CACTUS.

Meet the Insights XChange Podcast, proudly presented by Cactus Communications (CACTUS). Hosted by Nikesh Gosalia, President of Global Academic and Publisher Relations, this podcast features captivating interviews and insights from industry experts.

Why Listen?

In today’s fast-paced academic world, staying up-to-date with the latest trends and crucial issues is essential. Whether you’re an experienced researcher, a seasoned publication professional, or simply curious about the world of scholarly communication, this podcast will give you the information you need.

What’s in Store?

Every Insights XChange Podcast episode features lively discussions with academic experts. These conversations dive deep into pressing issues, offering fresh perspectives and actionable takeaways. From the future of open research to the potential of AI in publishing, this podcast covers it all.

Inaugural Episode

Our debut episode features a true pioneer. Nikesh Gosalia, President of Global Academic and Publisher Relations, will sit down with Ashley Farley, the Program Officer of Knowledge and Research Services at the Bill and Melinda Gates Foundation. Enjoy a captivating exploration of open research, the sustainability of the Article Processing Charge model, the game-changing potential of AI in publishing, and the pressing need for inclusivity in academia.

Ashley Farley brings a wealth of experience and insights about scholarly publishing, and her conversation with Nikesh promises to be both thought-provoking and enlightening.

Spotify link: https://ow.ly/FlHu50PRM0y

Apple Podcasts link: https://ow.ly/1rYv50PRM0A

Google Podcasts link: https://ow.ly/uR5U50PRM0B

YouTube: https://ow.ly/4rtU50PRMTT

Thank you for being part of our community. We can’t wait to embark on this captivating journey into the world of research and knowledge with you.

The post Launching the Insights XChange Podcast: Conversations Shaping Academia appeared first on CACTUS.

Watch the video

Transcript

Shivanee:

Hi, I’m Shivanee Shah, head of Content at Impact Science.

With the increasing number of papers being published, also comes a challenge for publishers to be able to scale up their peer review processes while ensuring to maintain quality and integrity in publishing. One way to make this process a little bit more efficient would be to introduce AI, with AI at the centre of so many different processes and fields.

Today, as part of the Peer Review Week, we have Dr. Marie Souliere to talk about the latest in AI and peer review. So, Marie has been heavily involved in developing Frontiers’ Artificial Intelligence Review Assistant, AIRA, and we are excited to hear her views on the intersection of AI and peer review.

All right, so thank you so much for being with us today, Marie. We’re really happy to have you here. And the very first thing that maybe we should start with is to talk about your motivation to exploring the intersection of AI and peer review.

What challenges did you see to the traditional peer review process that led you to consider AI-based solution?

Marie:

Thanks, Shivanee. It’s really a pleasure to be here. I think the first time we properly started exploring AI for peer review was back in 2017.

I remember I was sitting in a room with one of the Founders of Frontiers and the now Chief Technology Officer before another meeting, and we were talking about how we as publishers and our editors and reviewers, really needed help with peer review. Frontiers has always been really an innovation company. And at that time, we were thinking that with the exponential rise of research and research papers, as you mentioned, and reviewers were being more and more in demand, and some were performing really sparse reviews on papers, and we wanted to really make sure peer review would improve manuscripts to the best that they could be.

That’s always been one of the big aims of Frontiers with our unique collaborative peer review process. So, we needed to ensure peer review was strong and also that we provided reviewers in the community with support. We felt like we could help them by telling them maybe that the citations weren’t the best in the paper, that the images had issues, or that the statistical power wasn’t correct, for example.

And we were also getting feedback from editors that they really didn’t want to look at papers where they felt the language level wasn’t up to par. And, so, we wanted to help with all of these aspects.

We already knew from using the iThenticate software to look for language similarity for several years, that with scale comes a different level of challenges that can only, or mostly, be handled by workflows and algorithms. From there, me and my teams working on peer review and research integrity at the time, we created a pretty long wish list of quality and peer review aspects that we considered with the editors as well that needed to be supported by automation and artificial intelligence. We called it the Reviewer X. At the time, it was pretty funny.

We thought there would be a specific tab in the forum called Reviewer X instead of Reviewer 1, 2, or 3. And eventually, it became our Artificial Intelligence Review Assistant, AIRA. And I still have folders on my laptop that are called Reviewer X. I still cherish that name a little.

And, so initially, all of this really stemmed from the dual considerations of wanting to maintain high quality as this scale became more and more difficult for reviewers and ourselves to handle, with the same level of quality standards for publication. And the other main consideration was seeing that there were some particular challenges that could be solved only by AI.

So, we needed AI to really augment decision-making powers for us.

Shivanee:

It’s so good to see AI helping with scaling up and making workflows more efficient. But AI is sometimes viewed with a lot of distrust, especially amongst the scientific community. How were you and your team able to get buy-in from internal stakeholders about using AI in peer review?

Marie:

I think it’s healthy that this is the default setting. I don’t think we should blindly trust AI without knowing how it’s used, who created it, and what it is aimed at performing. So, I actually support this view and I think it’s up to the developers and the people providing these tools as a service to build the trust and the results for those who are using it.

As you mentioned in the introduction, I’m an elected council member for the Committee on Publication Ethics (COPE), where I’ve been somewhat of their AI expert for a few years. And I hosted a webinar specifically called ‘Trustworthy AI for the Future of Publishing’, a couple of years ago. In there, I was presenting the discussion document that we wrote with COPE that people can find online on their website. It’s on AI decision-making and publishing. And in there, we say that AI should not be making decisions on acceptance and rejection at this stage.

I also had in this webinar Nischay Shah, the CTO of Cactus Labs, who was presenting on leveraging AI to improve the quality of publications, and Ibo van de Poel, a Professor of Ethics and Technology at TU Delft, who talked about the realization of trust-ready AI and the need for fairness, accountability, and explainability.

And these points are basically what we did internally to get buy-in from our teams. We designed the tools with the IT teams in a way that was explainable, and we took time to explain really what the AI did and where it took data from to do that as well.

We then made sure that no decisions were made without a human in the loop. The AI would flag anomalies and a team member would have a look at it. It’s still the case. We also made sure the design of the system that could tell with the tool whether it made a mistake or not and in what way, so we could give the feedback directly to the tool.

And that’s an important part of building the trust with users when you can also provide the feedback to improve the algorithm. So, in a way, you see it as AI augments your human decisions, but human decisions augment the AI tool back.

And as I mentioned earlier, we had and still have a pretty long wish list of quality checks we wanted AI to help with. And currently, we have roughly 30 different checks with automation and 13 of those actually do use artificial intelligence technology, not just automation. And for each, when we would release them in the platform, we had a specific label that was literally called ‘untrusted checks’ that we could have a look to help train the AI, but we would not base a decision on these results yet, because they were deemed not trustworthy enough. And so, some of them remained untrusted for a year, some only a few weeks.

It always depended on feedback from the teams on how accurate and useful the tools turned out to be and then they would become ‘trusted checks’ with the specific label. And last year, we trusted enough several of these checks to release them to external editors and reviewers in our platform [who] are evaluating the manuscript. So, we show them the results from the AI and let them provide their own feedback as well.

So, with little also explanatory videos on how to use the AI and for each of the checks, details on what the AI detected. So that’s really the needed transparency that is part of developing trustworthy AI. So overall, it’s really a step-by-step process driven also by knowledge sharing and accuracy that can lead us to the trust.

Shivanee:

So, you mentioned using AI for quality checks. Can you also use AI at other stages in a journal’s workflow? And again, how can a journal decide when to use AI and when not to in order to maximize efficiency and productivity?

Marie:

I think that’s the beauty of artificial intelligence tools. They can really be used at any and every stage and we can tailor and design tools to assist in the workflows that we need.

Obviously, you need to find the tools that you need, or you can develop them yourself if you have the technology and the developers. So, different journals or publishers will need AI tools at different stages depending on their way of working. If you work for a journal that has 90% desk rejection rates for their efficiency, they should focus on initial manuscript quality tools to make an assessment and reject effectively early on.

For journals that have higher publication rates, that have more papers going to reviewers, or have open peer review, they might want to use more minimal validation tools to ensure a paper is the right structure and the right language. But then they might want to focus AI tools and support tools during peer review or after reviewer recommendations to do a final verification before publication, for example.

So, I would say a journal deciding where to use AI and what tools they should bring in should be based on where they see that they are having to do a large amount of manual work to detect issues or anomalies in a small amount of papers.

But that is something you really need to do because it’s a critical or major risk if you don’t catch it. I’ll give you an example. At Frontiers, we used to manually look at every single figure submitted with the manuscript.

Currently, we get about 20,000 figures per week, which would require so much time from the team members to individually look at. And the reasons for checking the images were not about the science, because we mostly entrust this to our editors and reviewers, but we were verifying for potential image manipulation, which is fraud, and a high risk if papers with issues are published. And another very high-risk situation we were looking for was to verify whether there were identifiable human images in the figures, for which we absolutely need a consent form from the person and usually that would be, you know, for patients and the paper would reveal medical information; so, it’s very important. These are problematic cases, and for the 2 to 3% of manuscripts that might have a gel image, a microscopy, a human image, we were manually looking at them all. So, what we looked into doing was to design our own AI tool with a machine learning segmentation model and point matching and clustering algorithms, the whole thing, to detect image integrity.

And we created a separate ML model to detect humans. And from there we trained both models, we tested them, we built the trust, and we were able to rely on this now for image integrity flag. So, it does have some false negatives with images flagged that don’t have issues. But we trained it to err on the side of caution to make sure we had the maximum recall.

We mean to make sure we don’t miss any true positives; we don’t miss any problematic cases. So, we now look at maybe 1000 images a week, so 5% of all of them, and we have the specific details and highlights on the figures from the tool, the AI tool, on what to look at. So overall, this resulted in a massive increase in efficiency, it’s a 100-fold decrease in the time spent on this particular task.

And this was a clear example for us of a choice we made to develop an AI tool for a particular task that we thought we were spending a lot of time on in our journal workflow and that required improved efficiency. I think as a plus, to conclude this little part, was also that we found out this AI tool sometimes does catch manipulations that we would not have caught ourselves.

So, that’s something else special about these tools. It’s this ability to again augment human decision-making in addition to improving efficiency.

Shivanee:

So, this is really interesting, especially when we know peer review can be so complex and subjective and there’s so much more that AI can do. I’m sure there’s a long way that we can go with using and developing AI to make things even more easier and more effective.

How can AI address these challenges while maintaining the quality and integrity of the review process? Could you share any specific features or mechanisms of peer review tools that would ensure unbiased evaluation?

Marie:

Well, I think first we have to get out of our heads the concept that AI is unbiased, because it has biases. They will be different ones, probably, than we humans have, maybe less cultural, less prejudiced. But there are biases in AI as well that are based on what data the AI is trained on and what the query or the lines of code selected to develop it were. For some aspects, yes. AI can be more blind, I would say, than we or editors are. As I say in my lab, less prejudiced with regards to author names, their country of origin, and it won’t have a personal relationship with the authors that would bias its assessment. So that’s a plus. And so, in theory, it has the potential to be less biased in evaluations. But the challenge we face is that AI is trained on existing data with decisions that were made by humans previously, and that does have some biases and they are really hard to abstract from the training models.

So, because historically more papers have been published from, for example, researchers in the UK, in Germany, in the US, the trained AI is likely to have some form of bias in favour or against papers with similarity to these, even if we don’t intend it as such. And sometimes we can develop biases we did not intend for as well, just due to the way it was trained.

I actually have a fun story I can share regarding this as well, about a bias I detected in one of our AI tools. It was some years ago. We developed a reviewer recommender that we still use today, but we’ve improved on it. It was done with machine learning technology and as part of our efforts to build the internal trust in the tool, I was cross-checking the results for accuracy and I started noticing a weird pattern, where reviewers, the top five recommendations there were often based in the same countries as the authors of the manuscript. So initially, I thought that must have been due to either big countries with a lot of researchers, or the opposite, but maybe they were very niche research fields.

For example, if you work on yellow fever or a specific type of fish in the northern sea, there are likely several experts in your specific country working on this as well. And I knew AI was also doing semantic matching between phrases in the manuscript abstract compared to abstracts of other papers. So, I thought this might be what was happening. But digging further, my conclusions didn’t hold as topics were not niche and I kept finding more and more cases with papers with Chinese authors and the top four or five recommended reviewers were from China. Then two researchers from Scotland recommended to edit a paper from a Scottish group, three reviewers from Norway with a paper with authors based there, and a lot of Italians as well, researchers with recommendations with Italian reviewers.

And I was very puzzled, and we studied this for a while, and in the end, we found out that it was based on specific terms or phrases used by researchers when writing in English. Italians writing research in English tend to phrase certain things differently than Americans, who do it differently than Scots or Chinese authors. And so, the semantic matching went beyond the matching of the content of the phrases, and it matched the language structure as well.

And this generated an unexpected bias that we had to address. And this was a very revealing example of the AI having learned something we didn’t expect and that acquired a bias we didn’t anticipate as well.

So, this story tells us that developers of AI tools really need to work with the researchers and publishers for the AI tools for publishing to see how best to address potential biases that are in databases and in the codes that we write to develop the AI or that might be generated by the AIs without the intention to do so.

And you can only find it out by testing. I think the biggest strength for peer review right now is the use of a combination of AI tools that might have some biases with human oversight and decision-making, where we will have other biases of our own, and the combination of both might even out to fairer and less subjective peer review overall.

Shivanee:

Right, that’s a really interesting bias that you mentioned and it’s cool that you picked it out and there are these opportunities then to go back and fix and improve the tool experience for whoever’s using it as well.

And I’m sure there’s a lot more that AI can do. How do you envision the future of peer review with the continued integration of AI? What changes or improvements might we see in the coming years?

Marie:

There is a lot going on with AI and in terms of publishing itself, we’re literally in a race between publishers and companies selling fake papers or manipulated research to be published. I think in the near future we’ll find out if they manage to completely outmanoeuvre us or if we manage to develop AI tools sophisticated enough to detect fraud at that level.

I think we’re doing good. We do have tools to detect manipulated images, weird citation patterns, some undisclosed conflicts of interest, some level of misconduct, statistical analysis issues, and some forms of peer review manipulation. But fully faked data and conclusions, there’s no tool that I know of right now that can detect that.

So, the overall issue is going to have to be tackled in another way by removing this need for fake papers to be generated in the first place. And this is a big task undertaken by a lot of organizations coming together, including COPE, STM, and others with institutions and governmental agencies around the world. And so, to answer your question, if those types of misconducts that have been really the focus of our AI developments and peer review for years now, to try to prevent fraud, if they finally start to diminish, we’ll have time and energy and efforts to focus on some of the promising improvements for peer review with a lot of standard quality checks that can be performed, I think on format style, language, citations and all the little flags for specific points that the reviewers can assess without having to look for all the details.

So, in parallel, I also foresee in the short term the use of generative AI as a very useful tool from the author’s side, with the ability to allow non-native English speakers or researchers with other language difficulties and disabilities to take advantage of writing tools. In the mid- to long-term, I personally believe that writing scholarly articles will be fully outsourced to generative AI, with researchers creating the studies, performing the research, yes, but the writing would be generated by AI and researchers would only validate it as accurate and take responsibility for content before it would be submitted to be validated by peers, and maybe Reviewer X, right.

So, I think there can be major advancements in how we disseminate research output and prepare it to be shared with the world, and doing the research will remain key and having it validated by peers as well, I think, as I don’t think we will trust AI to do either of these things for a long while. But the steps in between, of writing papers, of checking format, language citations, editing to fit a certain style, I think are all likely to be more and more just left to the new artificial intelligence solutions, so that researchers from everywhere can focus on what they do best, which is the scientific research itself for the benefit of mankind.

Shivanee:

Yeah. Wow, this is really cool. As a researcher a long time ago, writing was one of my weak points at that point.

I can imagine AI taking over all of those parts and making everybody’s lives so much easier. Researchers can be at par irrespective of their ability to write and to edit. Whereas they do excellent research, it’s just a matter of communicating this out. So, that’s great.

I mean, we have some things to look forward to. Thank you so much, Marie, for this informative session. I mean, we all learned where AI is and where it’s going to be next.

Thank you.

Marie:

Thanks, Shivanee.

The post AI in Peer Review—What Works and What Doesn’t? An Interview with Dr Marie Soulière appeared first on CACTUS.

Here, I will discuss some of the key benefits and challenges of content marketing for academic publishers and provide some best practices and examples to help you craft and implement an effective content marketing strategy.

Why content marketing matters for academic publishers

Academic publishing has never been more competitive and dynamic than right now, with even giant publishers wondering how to overcome the many challenges such as declining library budgets, changing user behavior, increasing open access mandates, and growing competition. To stand out from the crowd and build long-term relationships with their customers, academic publishers need to adopt a customer-centric approach that focuses on delivering value beyond their core products and services.

How a content marketing strategy can help academic publishers

Meet the needs of researchers and other stakeholders

Content marketing allows academic publishers to create and share content that addresses the career challenges, daily concerns, and interests of their target audience. Many researchers want to maximize the impact of their research, and often grapple with difficult questions such as how to conduct research effectively, how to publish in high-impact journals, how to secure funding, or how to manage research data. By providing useful and relevant information that helps their customers solve problems or achieve goals, academic publishers can demonstrate their understanding of their customers’ needs and build trust and loyalty.

As one example, Elsevier offers Elsevier Connect, a portal with a great deal of information relevant to researchers and other stakeholders. Their offerings include news, opinion pieces, and practical guides that address the key issues and questions that the research community faces.

Establish thought leadership

A thought leader is an individual or firm recognized as an authority in a specific field, who presents innovative ideas to influence others. Establishing thought leadership is already becoming an important consideration in higher education. Content marketing enables academic publishers to showcase their authority and credibility in their field by curating high-quality content that reflects their editorial standards and vision. By sharing insights, opinions, trends, and best practices on topics related to their discipline or industry, academic publishers can position themselves as thought leaders who not only inform, but influence and inspire their audience.

While the term “thought leader” is often applied to individuals, many major publishers are also acting as thought-leading organizations by amplifying voices within academia through new media channels. One example is the way that Nature Publishing Group has brought attention to many social issues in science with their Nature Careers Podcast or their extensive offering of opinion pieces on the problems that the world faces today.

Harnessing the power of open access

Open access publishing has challenged the old publishing paradigm and the push toward open access shows no signs of abating. Content marketing can help academic publishers leverage the opportunities offered by the growth of open access. Open access content may increase the reach and visibility of published content, as it can be accessed by anyone without barriers or restrictions. Moreover, non-restrictive open access licenses can generate more engagement and interaction from the audience, as their contents can be easily adapted and shared by other researchers or platforms.

As one example, Frontiers has been one of the major forces driving the open access movement, becoming one of the world’s most-cited publishers overall. They have made the most of this position by drawing attention to open access policy, which they have even made the topic of their most recent Frontiers Forum.

How to measure and analyze content marketing success

Content marketing is not a one-off activity, but a continuous process that requires planning, execution, evaluation, and improvement. To ensure that content marketing efforts are effective and aligned with organizational goals, it is crucial for research publishers to measure and analyze their content marketing performance using various tools and platforms, perhaps as part of a structured planning strategy like an OODA loop. Some of the key metrics to consider and how they may guide your actions include:

Traffic. How many people visit your website or platform? Is it coming from organic searches, social media, or other links? Do your readers tend to use desktop or mobile devices? Which countries are they accessing from? By assessing this, you can better target neglected markets within your field and assess how successfully you are making impressions.

Engagement. How much do people interact with the content? How long they spend on the page or platform? How many pages do they view per session? How many comments, likes, and shares do they leave? By understanding what gets the most engagement, you can tailor your content to maximize returns.

Conversion. Are your viewers taking the action that you desire, such as subscribing to a newsletter or blog, downloading a white paper or report, or registering for a webinar or event? Conversion rates allow you both to offer more relevant targets and to assess the efficacy of how you promote these targets.

Retention. What are the bounce and churn rates (percentage of visitors who leave after viewing only one page or who stop using a product or service, respectively)? How many customers remain active within a given period? By eliminating common factors that affect retention, such as slow page loading times or intrusive advertisements, you can boost retention and improve chances of conversions.

Impact. What is the impact of your content marketing on revenue, return on investment (ROI), and customer lifetime value? Looking at how your content marketing activities affect these metrics can help you further optimize to maximize impact metrics.

There are various tools and platforms that can help academic publishers measure and analyze these metrics. Google Analytics is free and offers powerful and simple-to-implement monitoring of traffic and engagement. HubSpot offers an extensive suite of tools for content marketing and measuring or improving conversion and retention. Finally, Altmetric is research-specific and can be used to see where the research you publish is making an impact.

Conclusion

Content marketing helps academic publishers connect with their target audience and deliver value beyond their core products and services, or even maximize the impact of their core offerings. By creating and distributing relevant content that meets the needs of the scholarly community, academic publishers can establish thought leadership and ultimately drive profitable customer action. Using the many analytic tools available on the market, publishers can react to changing trends and create bespoke strategies to offer better and more relevant content.

The post Content Marketing in Research Publishing: An Introduction appeared first on CACTUS.

In 2020, many researchers were forced to work from home (WFH) for a large part of the year, and the situation is recurring in 2021, with fresh Covid outbreaks and new lockdowns in different parts of the world.

In a 2021 study of WFH researchers, almost half reported reduced work efficiency. WFH has been particularly challenging for female and early career researchers as well as graduate students who have returned to homes in the Global South. During WFH, laboratory and field research tasks are obviously put on the back burner, and researchers devote time to catching up on the literature, data analysis, and writing. There’s a lot that publishers and societies can do to make peer-reviewed content easily accessible for a WFH audience.

Quick-to-process formats that allow multitasking

Younger researchers, particularly women, are more likely to be juggling childcare and work, resulting in lower productivity. Quick-to-process summaries of research, like infographics, help them stay on top of the latest findings without expending significant time in processing a lengthy research manuscript.

Podcasts and audiobooks also allow researchers to multitask while acquiring information, for activities like graduate school lectures and continuing education. Discussion-based podcasts can help at-home researchers cope with feelings of isolation from the scientific community, by providing them a semblance of the casual, yet meaningful conversations that take place at scientific conferences. 

Video captions

Videos are effective for summarizing the key takeaways of a study, thereby facilitating knowledge transfer. A large majority of video viewers prefer to watch without sound. Here’s where captions come in handy; in fact, a study by Verizon Media and Publicis Media found that 80% of viewers in the US are more likely to watch an entire video when it has captions.

Formats suitable for any kind of Internet connection

Researchers living in the Global South or even rural areas of the US often lack fast and stable Internet connections. They have difficulty accessing content like HD videos or livestreamed webinars. In fact, YouTube, Amazon, and Netflix announced in 2020 that they would reduce streaming quality worldwide, to lessen broadband strain. Organizers of conferences with livestreamed sessions and those publishing video summaries of research would do well to follow suit, by providing low-resolution options for video downloads.

Conclusion

Despite the many drawbacks of WFH, researchers are likely to prefer WFH or a hybrid home-office model in the future, even after lockdowns end. As they largely use time at home for reading the literature or writing, societies and publishers will find it worthwhile to make their content more accessible for an at-home audience.

The post Reaching At-Home Researchers: Tips for Societies and Publishers appeared first on CACTUS.