Conditioning factors for the scientific productivity of undergraduate students of health sciences at a private Peruvian University: A cross-sectional analytical study

Study design and population

An analytical cross-sectional observational study was conducted. The study population consisted of 4104 undergraduate students. The study included students of both genders enrolled in the 2020-I semester at the Faculty of Health Sciences of the Norbert Wiener Private University and who had taken research subjects. Students who did not wish to participate in the study and those who did not complete the requested information properly were excluded.

Type of sampling and sample size

A sample size of 400 students was calculated and a stratified probability sample was made by proportional allocation among the students who met the selection criteria of the study. A sample size was determined by proportional allocation according to the size of the school of origin, resulting in a total of 400 students, after applying the statistical formulas. The sampling was simple random probability sampling, since a list of all university students was available, ensuring a confidence level of 95% and a sampling error of less than ±5%.

Study variables

The independent variables included institutional culture, knowledge management and technological capital measured on a nominal scale. The dependent variable was scientific research productivity. To evaluate the study variables, questionnaires were designed based on previously established models and adapted by the author. The variable “Institutional Culture” was measured using a questionnaire based on the model by Rueda-Barrios and Rodenes-Adam (2016), which included 24 questions structured on a dichotomous response scale (1 = Yes, 2 = No). The questions were distributed across the following dimensions: participatory culture (12 questions), motivational culture (10 questions), and teamwork culture (2 questions). Similarly, the variable “Knowledge Management” was assessed with a questionnaire consisting of 13 questions also structured on a dichotomous scale (1 = Yes, 2 = No), covering the dimensions of socialization (5 questions), externalization (4 questions), and internalization (4 questions). Lastly, the variable “Technological Capital” was measured using a questionnaire comprising 15 questions, likewise formulated on a dichotomous scale (1 = Yes, 2 = No), distributed across the dimensions of R&D investment (5 questions), technological infrastructure (5 questions), and technological tools (5 questions). These questionnaires were developed to ensure a comprehensive evaluation of the study variables, considering both theoretical validity and the relevance of the selected indicators.

Procedures

The information from the participants was gathered through four questionnaires (Bonilla-Asalde et al., 2023b) based on the model of Rueda-Barrios and Rodenes-Adam (2016). All questions were posed according to a dichotomous qualitative scale (1=Yes, 2=No). Three questionnaires were applied for the independent variables. The first was composed of 24 questions on institutional culture and included participatory culture, motivational culture and teamwork culture as dimensions. The second questionnaire was composed of 13 questions on knowledge management and included the dimensions of socialization, externalization and internalization. The third questionnaire was composed of 15 questions on technological capital and included the dimensions R&D investment, technological endowment and technological tools. For the dependent variable, a seven-question questionnaire was applied with the dimensions of publications and visibility of the researcher. Data gathering was conducted via email during the period of March–August 2021.

The data gathering instruments were developed by the research team and validated by Aiken’s V coefficient, and the binomial test with the participation of ten methodological and thematic experts in university education who assessed the clarity, objectivity, updating, organization, sufficiency, adequacy, coherence, methodology and relevance of the content of the instruments. Reliability was assessed through a pilot test that included 30 students from the Faculty of Health Sciences of the Norbert Wiener Private University. The four instruments were subject to the Kuder Richardson test (KR-20) and a KR coefficient equal to 0.80 was obtained for the questionnaire on institutional culture; 0.82 for knowledge management; 0.79 for technological capital and 0.86 for scientific research productivity.

Data processing and analysis

The data obtained (Bonilla-Asalde et al., 2023a) were gathered in the Microsoft Excel 2010 software and were analyzed with the SPSS software version 25.0. Tables with absolute and relative frequencies were reported for categorical variables, while measures of central tendency and dispersion were calculated for quantitative variables.

To evaluate the relationship between each independent variable with the dependent variable, a bivariate analysis was performed using Pearson’s chi-square test with their respective p-values; values of p<0.05 were considered statistically significant. The prevalence ratio (PR) and its respective 95% confidence interval were calculated for each relationship.

Likewise, to evaluate the relationship between the independent variables and performance in scientific research, a multiple logistic regression analysis was applied, using the Stepwise method where the indicators that showed a value of p<0.05 in the previous bivariate analysis were added.

Ethical aspects

This study was carried out following the guidelines of the 1964 Helsinki Declaration and its subsequent amendments. This study was evaluated and approved by the Institutional Research Ethics Committee of the Norbert Wiener University on February 5, 2021, expedient 521-2021. All the participants in the study signed the informed consent form before their participation and their identity was anonymized for the elaboration of the database, so their integrity was not violated.

General characteristics of the study sample

A total of 400 students from the Faculty of Health Sciences of the Norbert Wiener Private University were analyzed, of which 74.5% (n=298) were male and 57.25% (n=229) were between 18 and 27 years of age. Also, 17% (n=68) were studying in the School of Human Medicine, while 5% (n=20) in the School of Human Nutrition. Besides, 78% (n=288) were in their sixth year of studies, while 0.5% (n=2) were in their eleventh year ( Table 1).

Descriptive analysis of the independent and dependent variables in the study sample

The indicators of institutional culture were evaluated in the sample studied. 70.75% (n=283) stated that there are no agreements for the dissemination of research. 76% (n=304) expressed the opinion that there is a concern for the personal growth of students, 65.5% (n=262) expressed interest in research and 60.25% (n=241) considered that there is no recognition of students who practice research by the university.

When evaluating the knowledge management indicators, it was found that 69.25% (n=277) consider that access to information is adequate. 77% (n=308) consider that there is adequate advice from teachers on research, while 72.5% (n=290) reported that there are no mechanisms for the dissemination of results. On the other hand, 62.75% (n=251) stated that they are not part of any scientific network and 82.75% (n=331) do not have access to indexed scientific journals. In addition, 60.25% (n=241) considered that the university does not use the results of research conducted by students.

Technological capital indicators were evaluated. It was found that 66% (n=264) consider that they do not have supporting human resources for research. 62.25% (n=249) stated that they had sufficient databases to conduct research, while 80% (n=320) considered that they did not have collaborative tools for research ( Table 2).

Table 3 shows the descriptive analysis of the dependent variable. It was found that 72.25% (n=289) have not published any article in indexed journals or congresses and 98.5% (n=394) do not have any research project approved for execution. Furthermore, none of the students in the study sample has an H index and only 2.5% (n=10) have at least one publication in Open Access ( Table 3).

Bivariate analysis of the independent variables and the dependent variable in the study sample

In the bivariate analysis, a statistically significant association was found between scientific research productivity and management commitment (CPR=1.493; 95%CI: 1.077–2.068; p=0.015), personal growth (PR=1.632; 95%CI: 1.041–2.558; p=0.024), receiving recognition for practicing research (PR=1.385; 95%CI: 1.012–1.896; p=0.043) and strategic alliances (CPR=1.422; 95%CI: 1.032–1.959; p=0.03). Regarding knowledge management, research proposals (CPR=1.522; 95%CI: 1.114–2.080; p=0.009), shared results (CPR=1.542; 95%CI: 1.120–2.122; p=0.01) and use of research results (CPR=1.436; 95%CI: 1.049–1.965; p=0.024) were significantly associated with scientific research productivity. In the technological capital dimension, the availability of human resources support (CPR=1.591; 95%CI: 1.165–2.173; p=0. 004), visibility of investment in research (CPR=1.481; 95%CI: 1.08–2.029; p=0.014), having equipment and software (CPR=1.482; 95%CI: 1.061–2.069; p=0.018) and laboratories (CPR=1.438; 95%CI: 1.047–1.974; p=0.024) ( Table 4).

Multiple regression analysis according to scientific research productivity in the study sample

The multiple logistic regression model found association between scientific research performance with management commitment (APR=1.392; 95%CI: 1.001–1.935; p=0.049) and availability of human resources to support research (APR=1.471; 95%CI: 1.063–2.063; p=0.02) ( Table 5).

Limitations

Several limitations of this study should be taken into account when interpreting the findings. First, the focus on students from Norbert Wiener University may introduce selection bias, as this sample might not adequately represent the socioeconomic and educational diversity found in other universities or regions of the country. This limited representativeness constrains the generalizability of the results to other contexts, institutions, or populations. Future research should consider more heterogeneous samples encompassing multiple institutions with varied characteristics, thereby enhancing the external validity of these findings.

Second, there is the possibility of information bias due to unmeasured variables that could act as confounders. Factors such as prior research training, availability of mentors, extracurricular support, or specific academic incentives were not fully captured. These omissions may influence the observed associations and highlight the need for more comprehensive data collection and analytical models in subsequent studies to better isolate the effects of each variable.

Third, the potential for participation bias cannot be overlooked. Students who chose to participate may differ systematically—in terms of motivation, previous research experience, or academic performance—from those who did not take part. Such differences could affect observed productivity levels and perceptions of institutional culture, knowledge management, and technological support. Future studies should consider strategies to improve response rates or employ sampling methods that minimize self-selection effects, ensuring a more balanced representation of the student population.

Moreover, the complexity of assessing scientific productivity extends beyond the metrics used in this study. Although indicators such as journal classification, the H-index, and the frequency of publications in indexed journals provide valuable insights, they do not fully capture the breadth and depth of research engagement. Future research might incorporate additional measures—such as the quality of mentorship, interdisciplinary collaborations, or early involvement in projects—to gain a more nuanced understanding of the factors driving student scientific productivity.

Despite these limitations, this study offers valuable contributions as one of the first nationwide to examine, in an integrated manner, the relationship between student research productivity and factors related to institutional culture, knowledge management, and technological aspects. By addressing dimensions seldom explored in the literature, these findings not only support a more holistic view of the elements influencing student scientific activity but also lay the groundwork for developing more effective academic policies and institutional strategies. Moreover, this work may inspire future research aimed at delving deeper into the internal dynamics of universities, comparing diverse contexts, and evaluating concrete interventions designed to enhance research training, engagement, and productivity among undergraduate students in the health sciences and beyond.