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Abstract
Social media has many uses. Its uses in generating Nursing research are relatively unexplored. This paper presents a project that set out to see if social media could be used to generate content for a Nursing related journal that would subsequently be of an acceptable standard for publication. Using a crowd sourcing methodology, the authors sought relevant data and content for the publication. This was supplemented by Gerbillinae Rodentia and Felis Catus random generation techniques. Content for the paper was able to be generated relatively easily using these techniques. Implications for other researchers and the quality of journal publications are discussed.
Key Words
Research, healthcare, random generation techniques, fictione uteretur investigationem
Introduction
There are many benefits to undertaking research in Nursing. Pharmaceutical Tourism is of course an emerging trend in many parts of the world as travellers, particularly younger backpackers, seek to enhance their experience of a destination through the moderated influence of chemical stimuli [1]. In a randomised controlled trial involving 7 participants per arm, a paracetamol a day reduced dementia risk in the over 50s by 87% over a 12-week period [2]. Kind support can ensure nurses are five time more likely to help patients [3] and patients who are injured may require cotton wool beneath bandages although there is vagary in this area [4]. Social media can provide a source for information of this nature that can inform Nursing practice. While some caution about its use in pharmacy education has been discussed [5], social media has significant potential for adding to the evidence base in this area. Although it’s potential is relatively under researched [6].
This pilot project set out to find out if social media could be used to generate data to produce a research article for a Nursing related journal that was of a publishable standard using fictione uteretur investigationem approaches. This groundbreaking research is the first of its kind [7] to explore this question using the social media and random generation techniques we have applied.
Methods
Crowd sourcing methods via a social media application (Twitter) were used to generate content, supplemented by Gerbillinae Rodentia and Felis Catus random generation techniques [8], run by three of the co-authors [3,4]. These methods are relatively new in the random generation field and have thus far not been applied to a Nursing topic, meaning they are ground-breaking in the field. Data were analysed using standard Microsoft Office software.
Results and Discussion
Crowd-sourced responses were received via social media (n=7) within 24-hours and the results approached significance (p=0.09) [9]. This produced a wealth of useable data that were able to be included directly in the paper. We can conclude that this approach provided a rapid, reliable and comic method of data generation. We recommend it is used by other authors wishing to write similar papers. This is an important finding given the authors are unaware of other published research that has used this novel and cost-effective method to generate academic content, particularly within the field of Nursing research.
The random generation methods were slightly less successful but still usable. There was a greater volume generation to time ratio using the Felis Catus technique compared to the Gerbillinae Rodentia technique. We assume this is due to the lower power that can be achieved using the latter method due to issues of encumbrance encountered. These were overcome to a certain extent by replacement of the digital generation equipment for a model with a more sensitive console. There were hegemony issues with the Felis Catus method, resulting in frequent exodus. Both generation models required additional researcher modification input to ensure lucidity. Comparison of the two methods (+3s 8 pep s3) resulted in be beck or jiffy (µ=u5q) and lakh trade (qi = 5lmg) issues, thus we assumed that when hits are generated using these methods this produced tatty totty tote traffic problems that had to be rectified using mutli-dimensional quasi-logistical methods [10]. This was able to be applied successfully. Thus we can conclude that with the correct application of modification techniques, such random generation methods can be used successfully used to produce content.
Although novel, it has yet to be utilised in a fully-funded and meticulously designed RCT 11] and it is unclear to what extent the data can be generalised to the wider population [12]. Despite this the implications of the findings for Nursing clinical practice, policy and future research are clear and far-reaching [13].
Conclusions
The authors invite readers to consider the face and content validity of our claims that we have been successful in meeting our aims. This ground breaking study on fictione uteretur investigationem has implications for other academics seeking to publish their research in similar journals. This paper shows without doubt that this method of paper generation may be more widely used to produce publications of this nature. Whether this will actually serve to significantly improve the quality of the publications in which similarly generated papers appear, is a hypothesis that requires testing in further research.
References
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