Bibliographic record
Abstract
If you think citations do not matter for nurse academics, then do not read on. There seems to be a ‘knee-jerk’ reaction among our colleagues to almost any mention of citations and this is often expressed as pearls of wisdom such as: ‘citations don't matter’; ‘impact factors of journals don't matter’; and ‘h-indices don't mean anything. This is often compounded by: ‘we need other ways of measuring the impact of publications’, but this is never followed up with any constructive suggestions. Take the h-index; two of us have written several editorials (Hunt, Cleary, Jackson, Watson, & Thompson, 2011; Thompson & Watson, 2010; Watson, McDonagh, & Thompson, 2017) on this and our most recent effort which admittedly did have some flaws in the initial data set–we duly and gladly corrected them elicited two critical responses (Porter, 2018; Rosser, 2017) and one article of support on the JAN interactive blog (McRae, 2017), for which we were very grateful. So, here we are again not only to emphasise the importance of citations and all things emanating from them, but to provide some advice on how you can increase citations to your work. The unknown factors may include the fact that nursing is a clinical subject. While practitioners may read our work, they do not generally write about theirs and, therefore, do not cite the work they read. This is possible but does not seem to have a negative effect on the citation patterns of medical journals. It is known that some subjects, for example history, emphasise the production of books rather than articles and, therefore, citation rates are low. However, this is unlikely to be a reason in nursing. Apart from undergraduate textbooks for which there is a large market in nursing, research is largely published in journals. One reason that is likely to contribute to relatively low citation rates in nursing is the nature of the field. In bench science and some topical and novel areas of medicine, the rate of discovery and turnover of research is much more rapid than in nursing. In these areas, completely novel discoveries are made, and existing discoveries are quickly augmented or even overturned. If the problem is behavioural—as in habitually not citing the work of others—then an immediate solution eludes us. We cannot influence the rate of discovery or the nature of our field. However, we consider that we can publish more citeable work, and that we can do more to increase citations to our work. Please note that we do not advocate inappropriate ways of inflating citations to our articles. These inappropriate ways include: excessive self-citation; encouraging or obliging our PhD students to cite us; or ‘citation clubs’ whereby a group of researchers agree to cite each other's work (Corbyn, 2008). Publishing an endless stream of trivial articles limited in terms of location, methodology, and sample size is not a strategy for increasing citations. While it is almost impossible to predict if any specific article will become highly cited, we do have the information to tell us which articles will tend to be highly cited. These include reviews and methodological papers. Among the remaining articles—those emanating from original research—the larger the study, the more important the topic it addresses and the rigour with which the topic is addressed all contribute. Here, the patterns are not so clear but large properly conducted clinical trials will win over cross-sectional surveys. Qualitative articles involving hard-to-reach samples about an important health issue will win over another focus group study of how nurses feel about being nurses. In addition, older papers are more likely to be cited and have had more time to accumulate citations and it is widely acknowledged that internationally co-authored papers tend to gain more citations (Morgan, 2013) and that open access articles are more highly cited than subscription only articles (Ottaviani, 2016; THE, 2014)). With specific reference to JAN, of the top 10 most cited articles in 2017, five were reviews; of the top 12 highest cited articles, seven were reviews. In nursing generally, in the same year, the first and third most cited articles, published in the International Journal of Nursing Studies, were reviews and the only JAN article in the top 10 was a review. This is also the case with other subject areas including chemistry, physics, economics, and business studies. Some people have always been good at promoting their publications; they were willing to put an effort into it and used the few means at their disposal to do this. One way was sending offprints of their articles to other researchers and a prime way was to ensure that they promoted their work and, thereby, their publications, at conferences. Other ways, in the early days of the internet and emails were to list latest publications as a signature to emails and to use whatever online facilities existed to make published work more visible. These approaches and the rise in social media over the past decade have led to ever more effective ways of promoting your publications (Smith & Watson, 2016). Taking Twitter® as an example, the ease with which a link to your latest publication can be sent out to your followers with an apposite quote, and perhaps a picture, was unimaginable when we three authors set out on our academic careers. And there is good reason for all academics to take this seriously; witness the significance which JAN ascribes to social media, and we are relatively late starters compared with some other journals, principally The Lancet. These journals put considerable effort into promoting their authors on social media because they know it is important and effective and consider that authors can also play their part in promoting their own work. There is evidence that the use of social media is effective and the correlation between mentions on social media—blogs and Twitter®—is known to be associated with higher cited articles (Eysenbach, 2011; Knight, 2014). Correlation, of course, does not mean causation and certain articles will be cited and generate interest on social media because they contain important and novel findings. There is no substitute for publishing good articles based on sound research. The Research Excellence Framework (REF) is the government's system for assessing research in UK higher education institutions (HEIs). It was first conducted in 2014 and replaced the previous Research Assessment Exercise. Its results inform the allocation of billions of pounds of public funds to HEIs (Research England, 2019). In 2021, UK nurse academics with ‘significant responsibility for research’ will have their research assessed in the next REF. To do this, the assessors will use peer review informed by citations. This supports the first principle of the Leiden Manifesto that quantitative evaluation should support qualitative, expert assessment (Hicks, Wouters, Waltman, de Rijcke, & Rafols, 2015). Following a competitive tendering exercise, Clarivate Analytics (https://clarivate.com/; accessed 1 March 2019) is the company which will supply the citation information for REF2021 (In REF2014 it was Scopus [https://www.scopus.com/; accessed 1 March 2019]). While at this stage it is unclear how panel members will use citation information, we would recommend that individuals should use every possible (ethical) approach to increase their publications’ citations count. To do otherwise is unwise since research is not complete until it is communicated again…and again…and again.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.291 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.084 | 0.134 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".