Submitted:
22 September 2024
Posted:
23 September 2024
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Preprints on COVID-19 and SARS-CoV-2
Abstract
This study aimed to test the hypothesis that the use of theories in open access resources during the COVID-19 pandemic would be greater than in copyrighted resources. Using the Scopus database from August 18-28, 2023, the search terms “COVID-19,” “Coronavirus,” “SARS-CoV-2,” and “2019-nCoV” retrieved 511,920 results, of which 17,487 were selected. After filtering, 8,254 studies were analyzed. These were categorized as "Both Theory and Model," "Only Theory," and "Only Model." Using MS Excel, the researchers sorted studies based on titles, abstracts, and keywords to assess the magnitude of theory and model use. The study found that theories and models were employed across various disciplines and indexed accordingly. Further analysis included term categories, year-wise distribution, and citation patterns in relation to access categories. The study recommended future research to explore the connection between theories/models and factors such as publication trends, geography, and funding. The study concluded that, journals should encourage authors to include theories/models used in their studies in titles, especially during health emergencies, to provide early insights into handling pandemics.
Keywords:
COVID-19 Pandemic
; Copyrighted Access
; Open Access
; Models
; Scholarly Communications
; Theories
Introduction
Scholarly publishing in the wake of the COVID-19 pandemic has witnessed a remarkable increase and change in publication trend among researchers (Miller, 2020). According to Else, (2020), scientists from across different domains—health and medicine, life science, physical sciences and engineering, social sciences and economics—raced to share research on and about the COVID-19 through preprints to the extent journals adjusted their policies of review process especially in preferences of COVID-19 related research over non-COVID-19 research. This agrees with submission of Palayew et al., (2020) who have demonstrated that, following the declaration of the COVID-19 as public health emergency of international concern (PHEIC) by WHO; there was a fast-track publication of articles changing the median time to acceptance from 93 to or less than 7 days. This is true, as the duration of time from acceptance to publishing has drastically reduced for medical journals by 49% (Horbach, 2020). This arose due to the fact that, countries around the globe have tried to flatten the curve of the spread of the virus since it has affected almost every aspect of life—economy, tourism, political affairs, arts, sports—to mention but a few resulting in an increased volume of publications especially from biomedical sciences (Aviv-Reuven & Rosenfeld, 2021). There existed equilibrium with respect to many ecosystems, but the pandemic disrupted and tampered with the way they operate due to the health crisis. In this perspective, perhaps this has to do with the study captured by Materska, (2022, p6) cited in Adakawa, (2022) who noted that, there are many ecosystems that encompass “information ecosystem, learning ecosystem, social ecosystem, socio-ecological ecosystem, cultural ecosystem, consumer ecosystem, searching ecosystem, innovation ecosystem, digital ecosystem, digital public services ecosystem, new media ecosystem, library ecosystem”. These ecosystems and others were affected and for them to regain their steadiness, communication scientific research is a necessity. To communicate scientific research effectively, researchers used theories and models to understand the behavior of animate and inanimate objects surrounding the pandemic with the sole aim of restoring the ecosystems to near or back to normalcy.
For instance, many researchers have realized that, the COVID-19 pandemic has disrupted everyday life, ways of running businesses, altered economies, etc. thereby resulting in developing resilient strategies. In this direction, using news media reports employing NexisLexis database, Le and Phi, (2021) have noted how, at the beginning of the first phase of the pandemic, the media reported mostly about the negative impacts of the COVID-19. At the same time, the hotels were using pro-active (such as saving), reactive (surviving), and proactive (recovery and innovation, and learning and transformation) strategies to develop resilience (Le & Phi, 2021). At the onset of every pandemic, infection will continue to claim more lives if theories and models are not put to test to understand the pattern of progression, transmission of the disease and pattern of acceptance of vaccines developed to curtail the spread of the disease-causing organism. This is true, as at the beginning of the COVID-19 pandemic, there was a slow pattern of using theories necessitating employing practical solutions to solve pragmatic problems. This might be attributable to the publishing procedure taking months to reach the public/audience.
Background of the Study
Pandemics are a meaning-generating phenomenon that reciprocates reversibly for calming down the nerves, ensuring safety, security, wellbeing of lives thereby adjusting the economic activities and restoring the global health to sustainably agreed equilibrium. This is true, as the pandemic uses to place humans in cognitive dissonance owing to numerous happenings that involve disruptions of everyday life necessitating the urgency to regain equilibrium as a matter of course. Theories are important in generating such meanings using empirical data resulting from investigating different facets of the pandemics. That is, pandemics are about creating “meanings at, on, about” at least five (5) levels described as quintuple helix. These levels are patients-medical-workforce-stakeholders-researchers-technologies. At each level, there are varying descriptions of the pandemics from different perspective, which can be scientifically, environmentally, socially, culturally, etc. inclined. These divergent views give rise to various and sometime diffused perceptions that spontaneously form cloudy atmosphere with the resulting droplets of fragments of facts whose scientifically evidence-based pieces of information pervades and strives. That is why describing the COVID-19 pandemic to the contemporary generation that witnessed it seems inadequate and to the future generation might look like exaggeration. This dichotomy emanates from the fact that, no matter how an observer tries harder to capture all the details of a particular phenomenon using available evidence, there is always a room for leaving a large portion of it not intentionally but because of the angle one takes as contained in special relativity explained by Albert Einstein in 1920s.
The objective of this research is to find out the magnitude of scholarly communication theories used during the COVID-19 pandemic.
Methods
The study investigated magnitude of scholarly communication theories used during the COVID-19 pandemic. The researchers used Scopus database from 18-28 August 2023. The search strategy used was “COVID-19 OR Coronavirus OR Coronaviruses OR SARS-CoV-2 OR 2019-nCoV” during the four (4) year period from 2019-2023. The search revealed 511, 920 results. Out of this number, 17, 487 results were retrieved. After filtering and pruning the data, 8,254 results were used for the purpose of this study.
Findings
Table 1 shows the distribution of occurrence of theories in the title (Both Theory and Model). It is obvious from the table that, protection motivation theory occurred frequently than other theories 7(10.44776%) in the title followed by grounded theory 5(7.462687%), theory of planned behavior 5(7.462687%), integrated theory of planned behavior and norm activation model 3(4.477612%), and theory and practice 3(4.477612%).
Table 2 shows the distribution of occurrences of theories in the “Only Theory” category in the title. In this table, conspiracy theories accounted for about 15% of the overall theories in this category followed by protective motivation theory 7(7%), and grounded theory 5(5%). In this perspective, it means that, during the COVID-19 pandemic, researchers inclined to investigate issues surrounding conspiracy theories as obstacles that restricted populace from using non-pharmaceutical interventions (NPIs), among others. It is important to mention in this juncture that, there are about 565 models used in the title to study COVID-19 related behaviors during the pandemic. The space is insufficient to contain all of them.
Table 3 shows the distribution of terms in the title across OA and CA articles. A chi-square test of independence was conducted to examine the relationship between the access categories (Open Access vs. Copyrighted) and the term categories in the title (Both Theory and Model, Only Theory, and Only Model). The result is x2 = 21.97; df= 2; P-Value=0.0001; ∝= 0.05]. These results indicate a statistically significant association between the type of access categories and term categories in the title, suggesting that the distribution of open access and copyrighted titles differs significantly across the different categories of titles.
Table 4 shows the distribution of the combined term categories year-wise in the title across OA and CA articles. In this table, the Chi-square value for the years 2020 to 2023 is as follows [x2 = 39.23; p-value = 0.0001; df = 11; ∝= 0.05]. The p-value for the combined distribution for the year 2020-2023 in the title is far less than the significance level 0.05 suggesting a statistically significant difference between the observed and expected frequencies. This implies that the differences between the content type and access type is not by chance. Likewise, the Cramers V value is 0.232, which is a weak to moderate association. While the association is statistically significant, it is not particularly strong indicating that the term category has some influence on the access category.
Table 5 shows the combined distribution of combined term categories year-wise in the abstract across OA and CA articles. The result for the Chi-square for the distribution is as follows: [x2 = 149.41; p-value = 0.0001; df = 11; ∝= 0.05]. The p-value for the combined distribution is exceptionally low, suggesting a statistically significant difference between the observed and expected frequencies across all categories and years. This implies that the distribution of OA and CA across term categories is non-random. This shows the likelihood of authors to use the term categories in the abstract or the journal policies recommend or remain mute about that. In order to know the strength of the relationship, Cramer’s V test was run. It was found that, it equalled to 0.172 suggesting weak to moderate association between variables. While the x2 test indicated a statistically significant difference, the strength of the association between term categories and access categories is not especially strong. This suggests that, while the relationship is not strong, other factors such as journal policies, publication date trends, funding agencies, etc. might be responsible for the weak to moderate relationships.
Table 6 shows the combined distribution of term categories in the author keywords across OA and CA articles. The Chi-square test results for the combined distribution for the year 2020-2023 across the categories are given as [x2 = 29.36; p-value = 0.0002; df = 11; ∝= 0.05]. The p-value is below the significance level 0.05, suggesting that there is a statistically significant difference between the observed and expected frequencies. This suggests a significant association between term categories and access categories. To understand the strength of the relationship, Cramer’s V test was conducted and the value of 0.194 was obtained. This value implies that, there is a weak association between the term category and access category. This means that, the term category does not have a significant influence on whether the access is OA or CA. That is, there no substantial and meaningful relationship between term categories and access categories, suggesting that, term categories can slightly determine whether the access category is OA or CA. This can serve as an evidence advising authors to include term categories within the author keywords section and can be associated with access categories alike. By implication, publishers and institutions can use this finding to fashion their OA policies. In addition, authors can use the term categories to enhance the accessibility, discoverability, and retrievability of their research outputs by using the term categories within the author keywords. Furthermore, there is evidence that, researchers can delve into understanding or exploring why certain term categories are strongly associated with OA or CA categories especially if they expand the spectrum of their studies to include such aspects as funding sources, journal policies, geographical locations of authors, among others. From another angle, funding bodies and institutions trying to increase OA content might need to consider including such term categories when developing policies. In this way, it implies that, the weak association suggests targeting term categories that could be an effective strategy for increasing overall OA publications.
Table 7 shows the distribution of combined term categories year-wise in the index keywords across OA and CA. The Chi-square test values are given as [x2 = 28.56; p-value = 0.0018; df = 15; ∝= 0.05]. The p-value of 0.018 is less than the significance level 0.05 suggesting that there is a statistically significant difference between the observed and expected frequencies. This implies that, the distribution of OA and CA across different term categories and years is not due to chance. The implications of this finding are many and diverse. The fact that, the p-value is less than 0.05 significance level implies that, there is a statistically significant difference between term categories and access categories suggesting that, the tem category influences whether an article is OA or CA. In addition, from year-wise perspective, the association may vary across years suggesting that different years exhibit different patterns of association between term categories and access categories. What can be deduced from the implications of this finding are many. Firstly, during the early phase of the pandemic, many authors tend to publish in OA journals due to the funding opportunities. This might have emanated from the decision taken by publishers and funders to make most research outputs public for containing the spread of the virus. On the other hand, for researchers investigating the publication trends, this finding is important in understanding factors (such as term categories) in influencing access categories. In terms of policy implications, institutions and funding agencies with goals of promoting OA might find this result interesting by focusing on term categories that are less likely to be made OA. To understand the strength of the relationship, Cramer’s V test value of 0.075 showed a weak association between variables in the dataset.
Table 8 shows the distribution of citation category in the title across OA and CA articles. The Chi-square test values are given as [x2 = 1638; p-value = 0.0001; df = 10; ∝= 0.05]. The p-value indicates a quite small value that is less than the significance level 0.05 suggesting a significant difference between the observed and expected frequencies. The p-value indicates a strong relationship between the range of citations and access categories i.e. whether article is OA or CA. The skewedness in distribution might be attributable to access restrictions, licensing agreements, or the nature of the content such that certain types of content might be OA at specific ranges but CA at others. Furthermore, understanding that certain categories could be OA or CA means that, institutions, libraries or publishers can make informed decisions about where to focus their efforts either increasing OA availability or managing CA items. A Cramer’s V test value of 0.805 shows a strong association between citation label and access categories. This implies that, the differences between these categories are substantial where knowing the citation label provides a strong indication of whether the content is likely to be OA or CA. This finding also confirms the significant association found in Chi-square.
These findings both confirm and challenge previous research. For example, Perianes-Rodríguez and Olmeda Gómez (2021) found that most European Research Council (ERC)-funded research is published in hybrid or non-OA journals (85%), which receive 50-60% of citations. This suggests that ERC-funded research is influential and that researchers with grants tend to avoid gold OA journals. Their study adds value by focusing on where ERC-funded researchers publish and why, complementing earlier studies that examined ERC's impact on areas such as gender, researcher mobility, and peer review (Perianes-Rodríguez & Olmeda Gómez, 2021). On the other hand, Bordons et al. (2023) examined the relationship between funding and OA in the Spanish National Research Council’s publications across three disciplines: Biology & Biomedicine (BIOL), Humanities & Social Sciences (HSS), and Materials Science (MATE). They found that BIOL had the highest OA share (66%), and funded research generally had higher OA rates than unfunded work, especially in experimental fields. International first authors also increased OA chances in HSS. About 50% of Web of Science articles are OA (Martin-Martin et al., 2018, cited in Momeni et al., 2021), with German institutions showing significant OA growth from 2010-2018 (Hobert et al., 2020, cited in Momeni et al., 2021). Studies like Sotudeh et al. (2015) highlight the benefits of APC models, with OA outperforming Toll access, gaining 21.36% and 49.71% citation advantages in 2009 and 2008, respectively. Natural Sciences saw the greatest citation benefit (35.95%), while HSS had the lowest (3.14%). International OA journals attracted more attention across multiple countries than domestic ones (Fukuzawa, 2017).
Table 9 show the distribution of citation in the title (Only Theory) across OA and CA articles. The Chi-square test results are as follows: [x2 = 1558.36; p-value = 0.0001; df = 11; ∝= 0.05]. From the results, it is visible that, the p-value is less than the significance level 0.05 suggesting that there is a statistically significant difference between the observed and expected frequencies. This means that, there is a significant association between citation range and likelihood the access category been OA or CA. The Cramer’s V test yielded 0.616 showing a moderate to strong association between the citation label and access categories, which implies that, the association is not random and highly associated with the citation ranges. This reinforces the Chi-square result of significantly meaningful association between the variables.
Table 10 shows the distribution of citation in the title (Only Model) across OA and CA articles. The Chi-square test results are [χ²=12978.93; p-value=0.0001; df= 35; ∝= 0.05]. The p-value is extremely less than the significance level 0.05 suggesting a statistically significant difference between the observed and expected frequencies. This implies a strong association between citation ranges and access categories. Similarly, a Cramer’s V test values revealed 0.544 suggesting that, there is a moderate association between citation category and access category. Despite presence of significant association between these variables, it is not strong enough as many other factors such as access restrictions, licensing agreements, nature of the contents, among others might influence the distribution of citation across access categories.
Table 11 shows the distribution of citation in the abstract (Both Theory and Model) across OA and CA articles. The Chi-square results are given as [χ²=13069.63; p-value=0.0001; df= 23; ∝= 0.05]. From these results, it is obvious that, the p-value is extremely lower than the significance level 0.05 suggesting that, the null hypothesis is rejected. This means that, there is a strong relationship between the observed and expected frequencies. For Cramer’s V test value, 0.553 indicates a moderate to strong association between the citation ranges and access categories, suggesting that, the citation range has a significant impact on whether access category can be OA or CA articles. This Cramer’s V test value reinforces the Chi-square test value.
Table 12 shows the distribution of citation in the abstract across OA and CA articles. The Chi-square results are [χ²=7275.34; p-value=0.0001; df= 13; ∝= 0.05]. As it can be seen that, the p-value is extremely small far less than the significance level 0.05 suggesting that the null hypothesis is rejected. This means that, there is a significant association between citation ranges and access categories. A Cramer’s value of 0.398 implies that there is a moderate association between the citation category and access categories. Despite the significant association, it is not a strong one.
Table 13 shows the distribution of citation in the abstract across OA and CA articles. The results of the Chi-square test are given as [χ²=44627.64; p-value=0.0001; df= 29; ∝= 0.05]. The p-value is far less than the significance level 0.05 suggesting the null hypothesis is rejected and that there is a strong relationship between the observed and expected frequencies. The Cramer’s V value of 0.320 indicates a moderate association between citation ranges and access categories. While there is a significant relationship, the strength of the association is moderate implying that other factors such access restrictions, licensing agreement, etc. may influence the distribution.
Table 14 shows the distribution of citation in the author keywords across OA and CA articles. The results of the Chi-square are [χ²=3417.59; p-value=0.0001; df= 14; ∝= 0.05]. The p-value is extremely less than the significance level of 0.05 suggesting that there is a statistically significant difference between the expected and observed frequencies and that the null hypothesis is rejected. A Cramer’s V test value revealed 0.721 suggesting a strong association between the citation ranges and access categories. This implies that, the citation range has a significant influence of determining whether the access categories could be OA or CA and it support the Chi-square test value of indicating strong relationship.
Table 15 shows the distribution of citation in the author keywords across OA and CA articles. The results of the Chi-square are [χ²=3413.53; p-value=0.0001; df= 14; ∝= 0.05]. The p-value is far less than the significance level of 0.05 suggesting that, there is a statistically significant difference between the observed and expected frequencies. That is, the null hypothesis is rejected, which means that, citation range has impact on determining what access categories could be (i.e. either OA or CA). A Cramer’s V value of 0.720 shows a strong association between the citation categories and access categories indicating that, citation category has a significant impact on whether the content is likely to be OA or CA items. In addition, this value confirms the χ² result of strong association between citation categories and access categories.
Discussions, Implications, and Insights for Hypothesis 1
To begin with, the current study is one the few studies desired during emergency especially of endemic, epidemic or pandemic nature where the confluence of uncertainty and confusion is eminent thus confounding to fill the atmosphere with doubts and the necessity for urgent solutions are extremely needed. This is true, as in the course of a health emergency, what the stakeholders in publishing/knowledge industry or health security sector need the most; is the presence of ingredients that can easily catalyze the application of knowledge to slow the phase at which the disease-organism travels and propagates. This is with a view to speeding up the period at which stakeholders can take important decision thereby making populace aware and adhere strictly to the guidelines, directing vaccine development, logistics delivery, boosting supply chain, lessening the spread of the viral/bacterial particles or any other disease-causing organisms, among others. When a pandemic erupts, many stakeholders perform their individualized and sometimes collective duties. From the knowledge industry, most of the findings above refer to them in one way or the other encompassing areas of journal polices, funding sources, geographic locations, publication venues, authors’ preferences, publication date trends, institutional policies, etc. For the health security sector, many roles are required that are reversibly shared with knowledge industry prior to reaching the populace for immediate compliance. One of the findings of this research is that, there is a pattern of increase in using theories/models from the beginning of the pandemic (2019-2020) to the post-pandemic period (2022-2023). This suggests that, if at the beginning of the pandemic, researchers focused on using theories/models, the pandemic would not have done more than it did to the population health and economy.
Bearing in mind that, research cannot solve all world’s problems, but extending a hand to attempt to contribute small fragments to the process of solving problems is a good thing. That is why this study attempts to look at a structure comprising five (5) key elements, namely patients—medical-workforce—stakeholders—researchers—technologies. These elements have roles to play during a pandemic. For instance, how the appearance of term categories such as theories or models in the title can affect its discoverability? To answer this question, it is a well-acknowledged fact that, many researchers while searching for documents/articles; the first interface they encounter is either the title or abstract. This means that, if the term category, for example theory, is not mentioned in especially title or abstract, the researchers may ignore important research output that could assist in providing a way forward to the ongoing research about the pandemic at the moment. In this way, stakeholders in health security sector should collaborate with knowledge industry in enumerating possible ways to suggest how authors should reconsider using these term categories in their write-ups to speed up the rate at which research can easily be discovered, understood, applied, etc. to solve a lingering health problem.
From the microscopic viewpoint the current research is trying to elucidate these quintuple points has to do with breaking down what the stakeholders, patients, medical-workforce, researchers, and technologies comprise. For stakeholders, they encompass health agencies (i.e. globally, internationally, regionally, nationally or locally), health providers (i.e. government- or private-owned hospitals, which include teaching or tertiary, cottage, general, specialist, specialized hospitals with their intensive care units). Healthcare or medical workforce comprises physicians (such as paediatricians, urologists, etc.), dentists, pharmacists, allied professionals (i.e. radiologists, physiotherapists, optometrists, medical laboratory scientists, basic clinical scientists, etc.), to mention but a few. Researchers are many but can be categorized into those working in dry laboratories, wet laboratories, social, economic, environmental, psychological, information, traditional and modern health surveillance researchers, among others. Technologies are still evolving in a rapidly increasing manner to supplement the activities embarked upon by all the above-mentioned categories and many more. Patients are those individuals from the population susceptible to diseases prevalent at the given time and can be categorized based on their demographic characteristics, which include but not limited to gender, occupation, education, status, age (children, young, elderly, etc.).
For instance, during a pandemic, taking pharmacists as an example, they require readily available information that has to do with drug discovery, drug evaluation, protease inhibitors, protein structure, viral non-structural protein, proteinase inhibitor, unclassified drugs, antimicrobial activity, computer-aided designs, crystal structure. The list is long and can contain enzyme activity, antiviral activity, complex formation, controlled study, drug efficacy, drug isolation, drug structure, drug targeting, high throughput screening, drug development, drug effect, molecular model, antiviral therapy, immunotherapy, vaccine, in vitro/in vivo studies, repetitive sequence, sequence analysis, sensitivity analysis, among others. At each level, certain important studies might be required that have to do with theories, or models or both to aid in speedy development of the desired anti-microbial agents. In addition, because the research of one component is needed by all other categories, research conducted by researchers, physicians, allied professionals, on or about patients can aid in a number of ways. In this way, the research outputs can contribute something that has to do with in relation with the disease at the time and African continental ancestry groups, Asian continental ancestry groups, European ancestry continental groups, Latin continental ancestry groups to understand the ethnically, ancestrally, environmentally, geographically diverse population. Furthermore, these researchers can come up with studies that have to do with genomic epidemiology study, virus antibody, immunization, immunogenicity, hospital admission, hospital mortality, mortality risk about middle-aged, elderly, children, adolescents, adults, prevalence of the infection, contact tracing, patient isolation, contact examination, immuno-compromised patients, population growth, population research, population risk, etc.
From the above, it is obvious that, making information readily available to the elements mentioned above is essentially important in understanding the disease and symptoms and possible ways forward to curtail the spread of the disease-causing organism. In this way, disease severity, binding affinity, disease transmission, infection control mechanisms, among others can best be understood. In addition, the symptoms can equip the elements of the structure with reliable information. For instance, does the patients have symptoms that have to do with coughing, diarrhoea, dysphagia, dysphonia, dyspnoea, face pain, fatigue, fever, headache, loss of appetite, nausea, vomiting, nose obstruction, otalgia, sore throat, thorax pain, etc. as in the case of COVID-19 patients. These symptoms of the disease can best be understood if the theories or models used in them are conspicuous to the researchers and stakeholders. The simplicity with which the diseases can be understood lies in the use of such term categories as elaborated by the current study.
Conclusions
During the COVID-19 pandemic, many researchers have employed the used of theories and models in their studies. Understanding whether term categories influence access categories (OA and CA) is important for decision-making, comprehending author preferences, journal policies, geographic locations, funding agencies, among others. There are instances where OA or CA is frequent. Mostly, journals that employ models have more CA in lower ranges and OA journals have citations in non-skewed distribution. The paper concluded that, journals should encourage authors to include theories/models used in their studies in titles, especially during health emergencies, to provide early insights into handling pandemics.
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Table 1.
Distribution of Occurrences of Theories in Both Theory and Model Category in the Title.
| Theory | No of occurrence | Percentage (%) |
|---|---|---|
| Adaptive structuration theory | 1 | 1.492537 |
| Burnout theory/model | 1 | 1.492537 |
| Classical test theory (CTT) and Item response theory (IRT) | 1 | 1.492537 |
| Connectivism theory approach | 1 | 1.492537 |
| Conservation of resources theory | 1 | 1.492537 |
| Conspiracy theory 5G | 1 | 1.492537 |
| Critical race theory | 1 | 1.492537 |
| Dempster-Shafer theory of evidence and grey relation analysis | 1 | 1.492537 |
| Dynamic capability theory | 1 | 1.492537 |
| Evolutionary theory of loneliness | 1 | 1.492537 |
| Extended theory of planned behavior | 1 | 1.492537 |
| Extended theory of value-identity-personal norm model | 1 | 1.492537 |
| Game theory | 1 | 1.492537 |
| Goal framing theory | 1 | 1.492537 |
| Graph theory | 1 | 1.492537 |
| Gratification theory | 1 | 1.492537 |
| Grounded theory | 5 | 7.462687 |
| Health belief model and theory of planned behavior | 1 | 1.492537 |
| Integrated theory of planned behavior and norm activation model | 3 | 4.477612 |
| Integrating the full spectrum of self-determination theory and self-efficacy into technology acceptance model | 1 | 1.492537 |
| Integration of pro-environmental behavior (PEPB) and service quality (SERVQUAL) | 1 | 1.492537 |
| Job demand-resources model and conservation of resources theory | 1 | 1.492537 |
| Kuznet curve theory | 1 | 1.492537 |
| Lattice field theory | 1 | 1.492537 |
| Life cycle theory | 1 | 1.492537 |
| Ogbu's cultural-ecological theory | 1 | 1.492537 |
| Online theory of change workshop | 1 | 1.492537 |
| Percolation theory | 1 | 1.492537 |
| Posthuman theory | 1 | 1.492537 |
| Practice to theory | 1 | 1.492537 |
| protection motivation theory | 7 | 10.44776 |
| Psychological capital theory | 1 | 1.492537 |
| Recognition theory | 1 | 1.492537 |
| Rhetorical arena theory and modality | 1 | 1.492537 |
| Self-determination theory (SDT) | 2 | 2.985075 |
| Serendipity-mindsponge-3D knowledge management theory | 1 | 1.492537 |
| Situational crisis communication theory | 1 | 1.492537 |
| Sociolinguistic theory of survival | 1 | 1.492537 |
| Statistical theory of epidemics | 1 | 1.492537 |
| Supply chain viability theory | 2 | 2.985075 |
| Terror management theory | 2 | 2.985075 |
| Theory and practice | 3 | 4.477612 |
| Theory of Planned behavior | 5 | 7.462687 |
| Theory of traditional Chinese medicine | 1 | 1.492537 |
| Theory-informed formative evaluation | 1 | 1.492537 |
| Uncertainty theory | 1 | 1.492537 |
| 67 | 100 |
Table 2.
Distribution of Occurrences of Theories in “Only Theory” Category in the Title.
| Theory | No of Occurrence | Percentage (%) |
|---|---|---|
| Actor-network theoretical study | 1 | 1 |
| Adaptive structuration theory | 1 | 1 |
| Antisemitic conspiracy theories | 1 | 1 |
| Application of Theory Planned Behavior (TPB) and Health Belief Model (HBM) | 1 | 1 |
| Burnout theory and measurement | 1 | 1 |
| Classical Test Theory (CTT) and Item Response Theory (IRT) models | 1 | 1 |
| Combined theoretical and experimental study of nordihydroguaiaretic acid | 1 | 1 |
| Connectivism theory | 1 | 1 |
| Conservation of resources theory | 1 | 1 |
| Conspiracy theories | 15 | 15 |
| Critical race theory | 1 | 1 |
| Dempster–Shafer theory of evidence | 1 | 1 |
| Dynamic capability theory | 1 | 1 |
| Evolutionary theory of loneliness | 1 | 1 |
| Extended theory of planned behavior | 1 | 1 |
| Extended theory of value-identity-personal norm model | 1 | 1 |
| Foundational theoretical adsorption and quinolone docking study | 1 | 1 |
| Game theory | 1 | 1 |
| Goal Framing Theory | 1 | 1 |
| Graph theory | 1 | 1 |
| Gratification theory | 1 | 1 |
| Grounded theory | 5 | 5 |
| Health belief model and the theory of planned behavior model | 1 | 1 |
| Integrated theory of planned behavior and norm activation model | 3 | 3 |
| Integrating health behavior theories | 1 | 1 |
| Job Demands-Resources Model and Conservation of Resource Theory | 1 | 1 |
| Lattice field theory | 1 | 1 |
| Life cycle theory | 1 | 1 |
| Migration theory | 1 | 1 |
| Ogbu’s Cultural-Ecological Theory | 1 | 1 |
| Online theory of change | 1 | 1 |
| Percolation theory | 1 | 1 |
| Posthuman theory | 1 | 1 |
| Practice to theory | 1 | 1 |
| Pro-environmental planned behavior (PEPB) and service quality (SERVQUAL) | 1 | 1 |
| Protection Motivation Theory | 7 | 7 |
| Psychological capital theory | 1 | 1 |
| Reaction–diffusion epidemic model and theoretical analysis | 1 | 1 |
| Recognition theory | 1 | 1 |
| Rhetorical arena theory | 1 | 1 |
| Self-determination theory | 3 | 3 |
| Serendipity-mindsponge-3D knowledge management theory and conceptual framework | 1 | 1 |
| Situational crisis communication theory | 1 | 1 |
| Sociolinguistic theory of survival | 1 | 1 |
| Statistical theory of epidemics | 1 | 1 |
| Supply chain viability theory | 2 | 2 |
| Terror management theory | 2 | 2 |
| Theoretical analysis of CF-Fractional model | 1 | 1 |
| Theoretical aspects of fiscal federalism and COVID-19 crisis | 1 | 1 |
| Theoretical characterization of iron (III) and nickel (II) complexes | 1 | 1 |
| Theoretical Design of Functionalized Gold Nanoparticles | 1 | 1 |
| Theoretical Docking of Medicines With Two Proteins | 1 | 1 |
| Theoretical framework and model of ICT adoption and inclusion | 1 | 1 |
| Theoretical Investigation of 5-Fluorouracil and Tamoxifen Complex–Structural and Docking Simulation | 1 | 1 |
| Theoretical molecular properties of Anisidine-Isatin Schiff bases | 1 | 1 |
| Theories of COVID-19 risky behaviors | 1 | 1 |
| Theorizing parallelisms between COVID-19 restrictions and strands of otherness | 1 | 1 |
| Theorizing sociomateriality | 1 | 1 |
| Theory and practice | 3 | 3 |
| Theory of Kuznet curve | 1 | 1 |
| Theory of planned behavior | 4 | 4 |
| Theory of traditional Chinese medicine | 1 | 1 |
| Theory-informed formative evaluation | 1 | 1 |
| Three key theories of omicron | 1 | 1 |
| Uncertainty theory | 1 | 1 |
| 100 |
Table 3.
Distribution of Terms in the Title across Open Access and Copyrighted Articles.
| Term Category | Open Access (Title) | Copyrighted (Title) |
|
|---|---|---|---|
| Both Theory and Model | 41 | 26 | 67 |
| Only Theory | 61 | 39 | 100 |
| Only Model | 447 | 119 | 566 |
| Total | 549 | 184 | 733 |
Table 4.
Distribution of Combined Term Categories Year-wise in the Title across Open Access and Copyrighted Articles.
Table 4.
Distribution of Combined Term Categories Year-wise in the Title across Open Access and Copyrighted Articles.
| Year | Content Type | Open Access | Copyrighted Access |
|---|---|---|---|
| 2023 | Both Theory and Model | 15 | 14 |
| 2023 | Only Theory | 18 | 14 |
| 2023 | Only Model | 151 | 32 |
| 2022 | Both Theory and Model | 10 | 10 |
| 2022 | Only Theory | 18 | 14 |
| 2022 | Only Model | 93 | 29 |
| 2021 | Both Theory and Model | 6 | 3 |
| 2021 | Only Theory | 15 | 5 |
| 2021 | Only Model | 92 | 17 |
| 2020 | Both Theory and Model | 6 | 3 |
| 2020 | Only Theory | 10 | 6 |
| 2020 | Only Model | 109 | 40 |
| Total | 543 | 187 |
Table 5.
Distribution of Combined Term Categories Year-wise in the Abstract across Open Access and Copyrighted Articles.
Table 5.
Distribution of Combined Term Categories Year-wise in the Abstract across Open Access and Copyrighted Articles.
| Year | Term Category | Open Access | Copyrighted Access |
|---|---|---|---|
| 2023 | Both Theory and Model | 193 | 94 |
| 2023 | Only Theory | 193 | 94 |
| 2023 | Only Model | 833 | 212 |
| 2022 | Both Theory and Model | 192 | 69 |
| 2022 | Only Theory | 191 | 70 |
| 2022 | Only Model | 693 | 171 |
| 2021 | Both Theory and Model | 113 | 36 |
| 2021 | Only Theory | 117 | 37 |
| 2021 | Only Model | 619 | 78 |
| 2020 | Both Theory and Model | 83 | 56 |
| 2020 | Only Theory | 93 | 56 |
| 2020 | Only Model | 593 | 138 |
| Total | 3913 | 1119 |
Table 6.
Distribution of the Combined Term Categories Year-wise in the Author Keywords across Open Access and Copyrighted Articles.
Table 6.
Distribution of the Combined Term Categories Year-wise in the Author Keywords across Open Access and Copyrighted Articles.
| Year | Term Category | Open Access | Copyrighted Access |
|---|---|---|---|
| 2023 | Both Theory and Model | 34 | 23 |
| 2023 | Only Theory | 35 | 23 |
| 2023 | Only Model | 113 | 37 |
| 2022 | Both Theory and Model | 37 | 17 |
| 2022 | Only Theory | 36 | 18 |
| 2022 | Only Model | 88 | 26 |
| 2021 | Both Theory and Model | 17 | 11 |
| 2021 | Only Theory | 17 | 11 |
| 2021 | Only Model | 61 | 9 |
| 2020 | Both Theory and Model | 15 | 13 |
| 2020 | Only Theory | 16 | 12 |
| 2020 | Only Model | 80 | 29 |
| Total | 549 | 229 |
Table 7.
Distribution of Combined Term Categories Year-wise in the Index (Database) Keywords across Open Access and Copyrighted Articles.
Table 7.
Distribution of Combined Term Categories Year-wise in the Index (Database) Keywords across Open Access and Copyrighted Articles.
| Year | Term Category | Open Access | Copyrighted Access |
|---|---|---|---|
| 2023 | Both Theory and Model | 59 | 11 |
| 2023 | Only Theory | 59 | 12 |
| 2023 | Only Model | 211 | 41 |
| 2022 | Both Theory and Model | 35 | 6 |
| 2022 | Only Theory | 36 | 5 |
| 2022 | Only Model | 226 | 34 |
| 2021 | Both Theory and Model | 38 | 13 |
| 2021 | Only Theory | 37 | 13 |
| 2021 | Only Model | 270 | 26 |
| 2020 | Both Theory and Model | 64 | 13 |
| 2020 | Only Theory | 63 | 13 |
| 2020 | Only Model | 343 | 56 |
| Total | 1441 | 243 | |
Table 8.
Distribution of Citation Category in the Title across Open Access and Copyrighted Articles.
Table 8.
Distribution of Citation Category in the Title across Open Access and Copyrighted Articles.
| Both Theory and Model in the Title | ||||
| Citation Category | Open Access | Copyrighted | Grand Total | |
| 0-24 | 104 | 181 | 285 | |
| 25-49 | 90 | 113 | 203 | |
| 50-74 | 72 | 50 | 122 | |
| 75-99 | 76 | 0 | 76 | |
| 100-124 | 107 | 0 | 107 | |
| 125-149 | 285 | 0 | 285 | |
| 150-174 | 333 | 0 | 333 | |
| 175-199 | 0 | 177 | 177 | |
| 200-224 | 220 | 0 | 220 | |
| 300-324 | 308 | 0 | 308 | |
| 400-424 | 413 | 0 | 413 | |
| Grand Total | 2008 | 521 | 2529 | |
Table 9.
Distribution of Citation in the Title (Only Theory) across Open Access and Copyrighted Articles.
Table 9.
Distribution of Citation in the Title (Only Theory) across Open Access and Copyrighted Articles.
| Only Theory in the Title | |||
| Citation Category | Open Access | Copyrighted | Grand Total |
| 0-24 | 165 | 216 | 381 |
| 25-49 | 271 | 113 | 384 |
| 50-74 | 72 | 116 | 188 |
| 75-99 | 164 | 0 | 164 |
| 100-124 | 207 | 0 | 207 |
| 125-149 | 284 | 142 | 426 |
| 150-174 | 495 | 0 | 495 |
| 175-199 | 177 | 0 | 177 |
| 200-224 | 442 | 0 | 442 |
| 300-324 | 308 | 0 | 308 |
| 400-424 | 416 | 0 | 416 |
| 500-524 | 522 | 0 | 522 |
| Grand Total | 3523 | 587 | 4110 |
Table 10.
Distribution of Citation in the Title (Only Model) across Open Access and Copyrighted Articles.
Table 10.
Distribution of Citation in the Title (Only Model) across Open Access and Copyrighted Articles.
| Citation Category | Open Access | Copyrighted | Grand Total |
|---|---|---|---|
| 0-24 | 1278 | 358 | 1636 |
| 25-49 | 571 | 133 | 704 |
| 50-74 | 771 | 51 | 822 |
| 75-99 | 353 | 97 | 450 |
| 100-124 | 324 | 0 | 324 |
| 125-149 | 705 | 0 | 705 |
| 150-174 | 1256 | 0 | 1256 |
| 175-199 | 197 | 176 | 373 |
| 200-224 | 835 | 0 | 835 |
| 225-249 | 243 | 240 | 483 |
| 250-274 | 538 | 0 | 538 |
| 275-299 | 1169 | 0 | 1169 |
| 300-324 | 1564 | 0 | 1564 |
| 325-349 | 1368 | 0 | 1368 |
| 350-374 | 2550 | 0 | 2550 |
| 375-399 | 1178 | 0 | 1178 |
| 400-424 | 1249 | 0 | 1249 |
| 425-449 | 860 | 0 | 860 |
| 450-474 | 1381 | 0 | 1381 |
| 475-499 | 2444 | 0 | 2444 |
| 500-524 | 505 | 0 | 505 |
| 525-549 | 1066 | 0 | 1066 |
| 575-599 | 585 | 0 | 585 |
| 600-624 | 1233 | 0 | 1233 |
| 625-649 | 646 | 0 | 646 |
| 675-699 | 2062 | 0 | 2062 |
| 700-724 | 1440 | 0 | 1440 |
| 775-799 | 788 | 0 | 788 |
| 800-824 | 813 | 0 | 813 |
| 875-899 | 882 | 0 | 882 |
| 925-949 | 945 | 0 | 945 |
| 1075-1099 | 1092 | 0 | 1092 |
| 1275-1299 | 1296 | 0 | 1296 |
| 1500-1524 | 3026 | 0 | 3026 |
| 1725-1749 | 3459 | 0 | 3459 |
| 2150-2174 | 2163 | 0 | 2163 |
| Grand Total | 42835 | 1055 | 43890 |
Table 11.
Distribution of Citation in the Abstract (Both Model and Model Only) across Open Access and Copyrighted Articles.
Table 11.
Distribution of Citation in the Abstract (Both Model and Model Only) across Open Access and Copyrighted Articles.
| Both Theory and Model in the Abstract | |||
|---|---|---|---|
| Citation Category | Open Access | Copyrighted | Grand Total |
| 0-24 | 1549 | 1270 | 2819 |
| 25-49 | 1885 | 305 | 2190 |
| 50-74 | 1728 | 413 | 2141 |
| 75-99 | 663 | 92 | 755 |
| 100-124 | 531 | 234 | 765 |
| 125-149 | 1514 | 142 | 1656 |
| 150-174 | 1754 | 0 | 1754 |
| 175-199 | 1470 | 373 | 1843 |
| 200-224 | 1678 | 0 | 1678 |
| 225-249 | 701 | 226 | 927 |
| 250-274 | 511 | 259 | 770 |
| 275-299 | 574 | 0 | 574 |
| 300-324 | 1545 | 301 | 1846 |
| 325-349 | 2337 | 0 | 2337 |
| 350-374 | 2538 | 0 | 2538 |
| 375-399 | 2698 | 768 | 3466 |
| 400-424 | 2060 | 0 | 2060 |
| 425-449 | 4358 | 0 | 4358 |
| 475-499 | 1950 | 983 | 2933 |
| 500-524 | 1537 | 0 | 1537 |
| 525-549 | 0 | 549 | 549 |
| 550-574 | 0 | 550 | 550 |
| 825-849 | 829 | 0 | 829 |
| 1875-1899 | 1886 | 0 | 1886 |
| Grand Total | 36296 | 6465 | 42761 |
Table 12.
Distribution of Citation in the Abstract (Only Theory) across Open Access and Copyrighted Articles.
Table 12.
Distribution of Citation in the Abstract (Only Theory) across Open Access and Copyrighted Articles.
| Only Theory in the Abstract | |||
| Citation Category | Open Access | Copyrighted | Grand Total |
| 0-49 | 3450 | 1559 | 5009 |
| 50-99 | 2334 | 562 | 2896 |
| 100-149 | 2045 | 376 | 2421 |
| 150-199 | 3224 | 373 | 3597 |
| 200-249 | 2379 | 226 | 2605 |
| 250-299 | 1085 | 259 | 1344 |
| 300-349 | 4566 | 301 | 4867 |
| 350-399 | 5979 | 1143 | 7122 |
| 400-449 | 7252 | 0 | 7252 |
| 450-499 | 1950 | 983 | 2933 |
| 500-549 | 2056 | 549 | 2605 |
| 550-599 | 0 | 550 | 550 |
| 800-849 | 829 | 0 | 829 |
| 1850-1899 | 1886 | 0 | 1886 |
| Grand Total | 39035 | 6881 | 45916 |
Table 13.
Distribution of Citation in the Abstract (Only Model) across Open Access and Copyrighted Articles.
Table 13.
Distribution of Citation in the Abstract (Only Model) across Open Access and Copyrighted Articles.
| Only Model in the Abstract | |||
| Citation Category | Open Access | Copyrighted | Grand Total |
| 0-99 | 26625 | 4505 | 31130 |
| 100-199 | 28451 | 1454 | 29905 |
| 200-299 | 26062 | 964 | 27026 |
| 300-399 | 56571 | 1852 | 58423 |
| 400-499 | 51014 | 1642 | 52656 |
| 500-599 | 30836 | 0 | 30836 |
| 600-699 | 31567 | 0 | 31567 |
| 700-799 | 14129 | 0 | 14129 |
| 800-899 | 13359 | 0 | 13359 |
| 900-999 | 14995 | 0 | 14995 |
| 1000-1099 | 11558 | 1041 | 12599 |
| 1100-1199 | 9331 | 0 | 9331 |
| 1200-1299 | 5107 | 0 | 5107 |
| 1300-1399 | 4056 | 0 | 4056 |
| 1400-1499 | 5744 | 2832 | 8576 |
| 1500-1599 | 12391 | 0 | 12391 |
| 1600-1699 | 1652 | 0 | 1652 |
| 1700-1799 | 5215 | 0 | 5215 |
| 1800-1899 | 1886 | 0 | 1886 |
| 1900-1999 | 1966 | 0 | 1966 |
| 2000-2099 | 6179 | 0 | 6179 |
| 2300-2399 | 9312 | 0 | 9312 |
| 2400-2499 | 7293 | 0 | 7293 |
| 2500-2599 | 5119 | 0 | 5119 |
| 2600-2699 | 2692 | 0 | 2692 |
| 2800-2899 | 2829 | 0 | 2829 |
| 2900-2999 | 8933 | 0 | 8933 |
| 4500-4599 | 4504 | 0 | 4504 |
| 7600-7699 | 7664 | 0 | 7664 |
| 13400-13499 | 13475 | 0 | 13475 |
| Grand Total | 420515 | 14290 | 434805 |
Table 14.
Distribution of Citation in the Author Keywords (Both Theory and Model) across Open Access and Copyrighted Articles.
Table 14.
Distribution of Citation in the Author Keywords (Both Theory and Model) across Open Access and Copyrighted Articles.
| Both Theory and Model in the Author Keywords | |||
| Citation Category | Open Access | Copyrighted | Grand Total |
| 0-24 | 391 | 273 | 664 |
| 25-49 | 425 | 195 | 620 |
| 50-74 | 379 | 51 | 430 |
| 75-99 | 249 | 92 | 341 |
| 100-124 | 207 | 0 | 207 |
| 125-149 | 277 | 0 | 277 |
| 150-174 | 648 | 0 | 648 |
| 175-199 | 353 | 197 | 550 |
| 200-224 | 435 | 0 | 435 |
| 225-249 | 0 | 226 | 226 |
| 275-299 | 585 | 0 | 585 |
| 325-349 | 339 | 0 | 339 |
| 350-374 | 358 | 0 | 358 |
| 375-399 | 0 | 378 | 378 |
| 500-524 | 522 | 0 | 522 |
| Grand Total | 5168 | 1412 | 6580 |
Table 15.
Distribution of Citation in the Author Keywords (Only Theory) across Open Access and Copyrighted Articles.
Table 15.
Distribution of Citation in the Author Keywords (Only Theory) across Open Access and Copyrighted Articles.
| Only Theory in the Author Keywords | |||
|---|---|---|---|
| Citation Category | Open Access | Copyrighted | Grand Total |
| 0-24 | 399 | 265 | 664 |
| 25-49 | 425 | 195 | 620 |
| 50-74 | 379 | 51 | 430 |
| 75-99 | 249 | 92 | 341 |
| 100-124 | 207 | 0 | 207 |
| 125-149 | 277 | 0 | 277 |
| 150-174 | 648 | 0 | 648 |
| 175-199 | 353 | 197 | 550 |
| 200-224 | 435 | 0 | 435 |
| 225-249 | 0 | 226 | 226 |
| 275-299 | 585 | 0 | 585 |
| 325-349 | 339 | 0 | 339 |
| 350-374 | 358 | 0 | 358 |
| 375-399 | 0 | 378 | 378 |
| 500-524 | 522 | 0 | 522 |
| Grand Total | 5176 | 1404 | 6580 |
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