Introduction examples with explanations

Introduction example 1

N.B. This paper does not include a separate Literature section, so relevant studies are discussed in the Introduction.

[topic and its importance:] Hospital environment including hospital and healthcare settings [1] has a pivotal impact on patients to meet them with a variety of needs, such as medical care and spiritual comfort [2, 3]. Within the hospital, the indoor air contains suspended particles of multifarious properties and mediates the short-distance and long-distance transmission of microbes made up of bacteria and fungi [4]. Studies have shown that the spreading of microbes is connected with microbial pollution and diseases [5–7]. Thereinto, Proteobacteria, Phylum Firmicutes are wildly distributed in hospital wards and Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus are the most common pathogenetic bacteria in nosocomial acquired infections which mainly transmits in the manner of hospital air [8–13]. The spreading of these bacteria widely in the air causes hospital-acquired infections and occupational diseases [14, 15], affecting children, aged people, chronic patients with a weak immune system, and healthcare workers [16]. Obviously, the increasing in the number or diversity of pathogens will certainly raise the risk of disease. Thus, the risk of infection from airborne pathogens is an important consideration that must be taken into account on hospital wards for individuals or groups [17].

[discussion of field; insights gained from previous studies:] In recent years, visible light has been shown to have multifarious effects on bacteria [18]. There were significant differences in the composition, abundance, and viability of microbial communities associated with household dust when exposed to sunlight [19]. For these three representative vital pathogenic bacteria of nosocomial infection, many studies have revealed the inactivation effect of visible light. For example, McClary and Boehm irradiated S. aureus with full spectrum light 6 hours and measured transcript abundance by RNA sequencing to find out that S. aureus was inhibited by full spectrum sunlight [20]. Exposing S. aureus, P. aeruginosa, and E. coli strains to a single blue laser irradiation (450 nm) and counting the number of living bacteria, De Sousa et al. concluded that blue light significantly inhibited the growth of bacteria under low-intensity conditions [21]. Additionally, the effect of yellow light on bacteria has been preliminarily explored. Among them, Weng et al. injected S. aureus to construct infected wounds in mice then treat it with yellow light and a ZnO composite, thereby achieving effective sterilization [22]. Zhao et al. utilized yellow light LED and MG1363-pMG36e-mCXCL12 in the treatment of the burn wounds in mice and found that skin pathogens were significantly reduced, excessive inflammatory response was inhibited, and the wound healing was promoted [23].

[identifying gap in the field:] Currently, there is still no research to evaluate the effect of yellow light on wards microorganisms. There are also few studies on the effect of yellow light on E. coli, S. aureus, and P. aeruginosa at transcriptome level. [study aim; how the gap will be addressed:] Hence, in the present study, we first performed high-throughput sequencing method to preliminarily identify the distribution and composition of common microorganisms on the yellow light-treated wards. In order to explore the effect of yellow light on specific pathogenic bacteria in wards, we selected 3 bacteria (including Gram-positive and Gram-negative bacteria) that were highly related with nosocomial infection. These pathogenic bacteria were detected by a viable count method. Meanwhile, we explored the effects of yellow light on E. coli, S. aureus, and P. aeruginosa by conducting the transcriptome analysis of these pathogens. [contribution to the field:] These studies will help to reveal the effect of yellow light on the microorganism in the air of hospital wards and the transcription level of related pathogenic bacteria. This study provides a theoretical basis for the effective control methods of the common pathogenic microorganisms, guiding the prevention of ward infectious diseases, which is of great significance to reduce the nosocomial infection and transmission.

Explanation

• The first part of this Introduction explains why the topic is important: hospital indoor air affects the transmission of microbes, which influences pollution and diseases.
• Next, the author discusses insights gained from the field so far (light can be used to inhibit bacterial growth).
• The author then explicitly identifies the gap in the field (the effect of light has not been explored in relation to ward bacteria).
• The author explains how the study addresses this gap (it investigates the effect of yellow light on three bacteria in wards).
• Finally, the author explains how the study contributes to the field (it provides a theoretical basis for the practical prevention of ward infectious diseases).

Introduction example 2

[topic and its importance:] Considering the effects of cell phones on our lives and the amount of distraction they cause, it is imperative to increase research efforts on the influence of technology on human relationships. The number of people facing the ill effects of technology has been rising in recent years. According to a 2014 research report, 42% of unmarried participants in romantic relationships and 25% of partnered and married participants stated that their partners spent more time engaging with their mobile phones while they were with each other [1]. A 2015 survey reported that 90% of U.S. respondents had been on their phones while participating in the most recent social activity and 86% reported seeing others do the same [2].

[defining term:] “Phubbing” is the term used to describe a situation in which individuals spend time checking their cell phone during a conversation, which leads to neglect in interpersonal communication [3]. The word phubbing was created by merging two words, snubbing and phone, and describes excessive smartphone use [4]. Similarly, to be “phubbed” is to be neglected by another individual who is engaged with their cell phone [5]. When this situation occurs with a significant other or a spouse, it is called partner phubbing or Pphubbing.

[discussion of field; insights gained from previous studies:] Cell phones have become so ubiquitous that phubbing is nearly inevitable [6]. According to the findings of a study accomplished among college students by Karadağ et al. [7], the nature of smartphones makes phubbing unavoidable because they have the features of computers, with Internet access. This multidimensional structure leads to phubbing, which is a problematic issue. [identifying gap in the field:] A literature review showed that no previous research has investigated whether some, none, or all of the dimensions that lead to phubbing among students and their colleagues would also apply to phubbing among married couples. Additionally, few studies have reviewed the causes of phubbing in terms of several digital problematic uses [7–9], and few studies have examined the effect of phubbing on relationship satisfaction [10–15]. These studies have investigated the phenomenon from the perspective of one partner and neglected the other.

Thus, the current literature is a little meagre regarding this relatively new phenomenon, and no study has investigated phubbing’s possible predictors from the phubbing partner’s perspective or the impacts of such behavior on the partner being phubbed. The use of such dyadic data offers a better understanding of dyadic practices that rule the functioning of these variables and the way the procedure could function mutually between couples. In addition, very few studies have investigated the moderating role of factors in this relationship [13, 15]. [study aim; how the gap will be addressed:] Studying all these variables is essential in providing a complete picture of this multifaceted phenomenon, which is the aim of this research. In light of the foregoing perspectives, the current research intends to investigate a more integrative model to highlight the factors that influence phubbing and its impact on relationship satisfaction.

Explanation

• The Introduction starts by explaining the importance of the topic: human relationships are increasingly affected by cell phone use.
• The author then defines the term ‘phubbing’, the main focus of this paper.
• Next, the author briefly discusses the field by highlighting a study that has shown how common ‘phubbing’ is.
• The author then explains the gap in the field (to date, the literature has not explored the effects of phubbing on marriages or relationship satisfaction, or moderating factors).
• Finally, the author explains the aim of this paper (to find out what issues affect phubbing and what is the impact of phubbing on relationship satisfaction).

Introduction example 3 (shortened)

[topic and its importance:] Obesity is more prevalent in patients with schizophrenia than in the general population [1, 2], even after controlling for age, gender, and psychiatric practice attended [3]. Obesity is also an independent risk factor for multiple chronic conditions, including cardiovascular diseases, diabetes, hypertension, and stroke [4–6]. Among patients with schizophrenia, the significant predictors of obesity include gender, education level, smoking behavior, type 2 diabetes, a higher level of triglycerides [7], and antipsychotic medication [8]. While a patient’s body mass index (BMI) is a marker for their nutritional status, it does not reflect the changes in their body composition. The consideration of detail of body composition is significant because, although BMI is significantly correlated with fat mass, the value can be misleading depending on the individual level of adiposity [9].

[discussion of field; insights gained from previous studies:] In recent years, the Bioelectrical Impedance Analysis (BIA) method has been more frequently used to measure body composition variables. BIA is a better indicator of obesity than BMI in patients with schizophrenia [10]. It works with the principle that the transit time of a low-voltage electric current through the body depends on the characteristics of individual body composition [11]. While the dual-emission X-ray absorptiometry (DXA) is the contemporary reference method for the body composition assessment, as it has proven more accurate body composition components [12, 13], accessibility to this sophisticated measure is not always feasible. Using portable BIA equipment is preferable in this study because it is invasive, inexpensive, relatively quick to operate, and suitable for examining a large number of subjects.

[…]

[identifying gap in the field:] While BMI studies have been conducted among patients with mental health disorders in Indonesia [16–18], body composition studies have not been conducted. The proximation of obesity or body fat among patients with schizophrenia in the country may therefore be erroneous with available data on BMI, and management protocols may not be well informed. [study aim; how the gap will be addressed:] The main objective of this study was to measure BMI and body composition (using BIA) among psychiatric inpatients with schizophrenia and compare them with healthy controls. [contribution to the field:] To the best of our knowledge, this is the first targeted study ever conducted in Indonesia. Based on the findings of previous related studies and theories, we hypothesize that the patients with schizophrenia have higher BMI, total body water, and body fat but lower muscle mass, bone mass, physique rating, and basal metabolic rate compared to the healthy controls.

Explanation

• The author first introduces the topic (obesity in patients with schizophrenia) and explains why this is important to investigate (it is a risk factor for chronic conditions).
• Next, the author briefly discusses the field: recent studies have shown that for patients with schizophrenia, BIA is a better indicator of obesity than BMI.
• This is followed by the gap in the field: body composition (using BIA) has not been measured among patients with schizophrenia.
• The author then explains that the aim of the study is to bridge this gap; to measure BMI and body composition (using BIA) among this target population and healthy controls.
• The last part emphasizes the contribution of this work (it is the first targeted study in Indonesia to explore this issue).

References

Introduction 1: Xuanqi Zhao, Jing Wei, Wenjie Chen, Xuan Xu, Ruizhe Zhu, Puyuan Tian, Tingtao Chen, "Effects of Yellow Light on Airborne Microbial Composition and on the Transcriptome of Typical Marker Strain in Ward", Disease Markers, vol. 2022, Article ID 8762936, 11 pages, 2022. https://doi.org/10.1155/2022/8762936

Introduction 2: |Shuaa Aljasir, "Present but Absent in the Digital Age: Testing a Conceptual Model of Phubbing and Relationship Satisfaction among Married Couples", Human Behavior and Emerging Technologies, vol. 2022, Article ID 1402751, 11 pages, 2022. https://doi.org/10.1155/2022/1402751

Introduction 3: M. Marthoenis, M. Martina, Rudi Alfiandi, D. Dahniar, Rini Asnurianti, Hasmila Sari, Jacqueline Nassimbwa, S. M. Yasir Arafat, "Investigating Body Mass Index and Body Composition in Patients with Schizophrenia: A Case-Control Study", Schizophrenia Research and Treatment, vol. 2022, Article ID 1381542, 7 pages, 2022. https://doi.org/10.1155/2022/1381542

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