Articles

Attitude Towards Violence in the Post-Pandemic

Cruz García-Lirios^1*, María del Rosario Molina González^2
1Universidad de la Salud, CDMX, Mexico.
²University of Sonora, Navojoa, Mexico.

Article Info

Received Date: 17 February 2025, Accepted Date: 26 February 2025, Published Date: 28 February 2025

*Corresponding author: Cruz García-Lirios, Universidad de la Salud, CDMX, Mexico.

Citation: C García-Lirios, María del Rosario M G. (2025). Attitude Towards Violence in the Post-Pandemic. Journal of International Surgery Case Reports, 1(1); DOI: http;/02.2025/JISCR/004.

Copyright: © 2025 Cruz García-Lirios. This is an open-access article distributed under the terms of the Creative Commons Attribution 4. 0 international License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract:

Adherence to treatment is a central axis in the health agenda. In the context of the SARS CoV-2 pandemic, it is necessary to predict this phenomenon. The objective of this work was to contrast a structural equation model. A confirmatory, psychometric and cross-sectional work was carried out with a selection of 100 students, considering their participation in social service and professional practices in public hospitals in central Mexico. A factorial structure was found that explained 62% of the total variance, suggesting the contrast of the model in other scenarios.

Keywords: Covid-9; respiratory diseases; self-care; adherence to treatment

Introduction

The theory of mediating variables in the study of the determinants of violence seeks to explain how certain factors (determinants) influence the emergence of violent behavior through intermediate mechanisms (mediating variables). These theoretical models are essential to understand not only what factors predispose to violence, but also how these factors are transformed into violent behavior (PALACIOS-SERNA et al., 2024). Over time, various approaches and theories have emerged to explain these processes.

The systematic study of violence began from the interest in understanding how environmental stimuli influence aggressive behavior (MENESES REYES & POGLIAGHI, 2022). Albert Bandura's (1960-1970) social learning theory was fundamental in this approach. Bandura proposed that aggression was not only the result of instinctive impulses, but was learned by observing and modeling the behavior of others. Here, important foundations were established for identifying mediating variables, such as:

Another influential theory was Dollard and Miller's frustration-aggression theory, which proposed that aggression is a natural response to frustration (RAMOS RODRÍGUEZ, 2021). Mediating variables in this model include psychological factors, such as the accumulation of frustrations or perceptions of injustice.

Urie Bronfenbrenner's ecological model was instrumental in introducing multiple levels of analysis in violence studies (CORTAZA RAMÍREZ et al., 2023). In this framework, determinants of violence (such as poverty, exposure to media violence, or family abuse) are mediated by contextual and personal factors. Mediating variables include:

More recently, interest has focused on interactions between biological, psychological, and social factors (BOLAÑOS, 2017). Contemporary theories suggest that violent behavior is the product of complex interactions between genes, neurobiology, and the social environment. Examples of mediating variables in this approach include:

Mediating variables are the mechanisms through which determinants (risk factors) influence outcomes (such as violence). A classic mediation model involves three components (SOBERANO SERRANO, &; DELHUMEAU RIVERA, 2019): A factor that predisposes to violence, such as poverty, discrimination, or experiences of abuse. The intermediary mechanism through which the determinant operates. Examples include cognitive beliefs, chronic stress, and conflict resolution skills. In this case, violent behavior is the dependent variable.

Beliefs about the legitimacy of violence to solve problems may mediate the relationship between exposure to media violence and aggressive behaviors (FERNÁNDEZ DE JUAN & FLOREZ MADAN, 2018). The development of a moral ethic may mediate the impact of an abusive upbringing on the propensity to violence. Skills to manage emotions such as anger may mediate between traumatic experiences and violent behaviors. People who experience social exclusion may develop feelings of anger or hopelessness, which may mediate the relationship between social rejection and violence.

The study of mediating variables of violence has evolved from simplistic explanations to complex models that incorporate the interaction between individual, social and biological factors (MARTÍNEZ VERDÚ, 2022). The identification of these variables is not only key to understanding how and why violence arises, but also to designing effective interventions to prevent its occurrence.

As of April 2023, 100 million people have been infected and three million have died worldwide from the SARS CoV-2 pandemic and the Covid-19 disease so far (SANCHEZ et al., 2022). In Mexico, even though the records of minors are recognized by the health authority, 170,000 people have died. The vaccination policy followed the United Nations Covaxx mechanism (GARCIA LIRIOS, 2022a). It is a multilateral agreement where governments contribute a fund that is distributed according to the criteria established by the World Health Organization. It involves the purchase of mats to immunize 10% to 50% of the population, following a logic of availability, production and equitable distribution among the 34 signatory countries. In addition, Mexico has negotiated directly with multinationals and the governments of Russia and China for the management of vaccines. 75 million were purchased from the Oxford company, 20 million from Covaxx, 15 million from Pfizer and 30 million from Cansino, although it has made agreements with others based on compliance with the agreement, availability and supply of vaccines. The Ministry of Health has established medical personnel as a priority for immunization until February 2021, followed by the elderly sector until April, people aged 50 to 59 until May, 40 to 49 years until June and the rest until March 2023.

However, information that discredits the government has spread the association between vaccines and deaths without documenting studies, reducing the data to a coincidence and without considering the risks of exposure to immunization with comorbidities (CARREON et al., 2022: p. 51). Therefore, empirical testing of a model that allows studying the effect of non-scientific information on treatment adherence decisions is essential to observe institutional health responses.

BUSTOS, JUAREZ & GARCIA (2022) showed that the norms of the work context influenced personal care and the prevention of work accidents, mediated by the perception of risks and internal security policies. Beliefs and attitudes towards self-care are determinants of adherence to treatment. In the case of respiratory diseases, this process is attenuated by the perception of risk that is proposed to be elucidated in this study.

GARCÍA LIRIOS, (2021c) suggest that adherence to treatment is reduced by agents external to social and family support that configure adherence in barriers. Evaluations can be favorable when it comes to diseases that do not warrant a prolonged stay. Positive or negative assessments of the disease can be complemented by decisions about personal, family, partner and other group care. Intentions are another attitudinal dimension that has not been explored, described or even explained. The state of knowledge indicates that there are two theories responsible for intentions.

JUAREZ NAJERA et al., (2020) found that age and adverse reactions to treatment reduce adherence and increase adherence to barriers. The uncertainty of risks favors the search for low costs and high benefits, compared to the certainty of risks that favors the search for low costs and high benefits. That is, when diseases are uncertain, people run the risk of obtaining expensive solutions.

GARCIA LIRIOS et al., (2022) demonstrated that adherence to treatment is related to quality of life expectations since the triggers of risk are beliefs and intentions about a disease. In this sense, the present study explains the uncertain attitudes towards respiratory diseases in relation to the groups in which individuals are immersed.

The contribution of the study to the state of the art lies in the contrast of a model that studies the determinants of adherence to treatment (GARCIA LIRIOS, 2021a). Consequently, the modeling and contrast of the relationship between the variables integrates the findings of other research. Therefore, the objective of the research is to show the prevalence of psychosocial determinants of public health focused on respiratory diseases in four main lines of knowledge related to regulation, policies, health promotion.

Are there significant differences between the dimensions of treatment adherence reviewed in the literature as effects of non-scientific information with respect to the contrast of the model proposed in the present work?

The premise that guides the present work indicates that adherence to treatment had been considered as part of a social, work and family support structure in rehabilitation, but not as an effect of unscientific information disseminated in the media, electronic networks such as Facebook, Youtube, Twitter and WhatsApp (GARCIA,BERMUDEZ & JUAREZ, 2022). In this sense, the diversification of factors that make up the adherence process implies the convergence of the media spheres with the personal, family, work and institutional spheres (GARCIA LIRIOS, 2021b). In this way, the adherence structure will include dimensions related to each area, as well as the allusion to their hybrid combinations such as vaccines, their percentage of effectiveness and the attribution to the government that people associate with the quality of immunization.

Method

Design. Since the pandemic led to a strategy of confinement and prolonged social distancing, a cross-sectional design was chosen. In addition, a psychometric study was carried out to establish the cognitive and behavioral variables associated with the rejection or acceptance of immunization.

Sample. A non-probabilistic selection of f 100 students (56% women and 44% men, M = 21.2 SD = 1.23 age and M = 9,872.12 USD SD = 234.35 USD per month) from a public university was made. The selection criteria were to belong to the system of internships and social service in organizations and institutions with and without profit in the municipality of Chimalhuacán, State of Mexico.

Instrument. The scale of adherence to the treatment of chronic degenerative diseases was used, which includes 40 statements (elements) around regulations (“Covid-19 treatment is effective in traditional people”), values ​​(“A person who follows the recommendations of Covid-19 experts”), beliefs (“Covid-19 affects crazy people”), perceptions (“The Sputnik V vaccine has more risks than benefits”), knowledge (“Covid-19 affects decisions”),  skills (“Covid-19 can be controlled with a balanced diet”), attitudes (“The Sputnik V vaccine affects older adults”), decisions (“I am going to get vaccinated against Covid-19”), intentions (“Choose the Sputnik V vaccine to prevent Covid-19”) and behaviors (“I get vaccinated against Covid-19”).

Procedure. The judgment was made using the Delphi method, as well as the symptom technique for the analysis of assessments by expert judges. In three rounds the content of the items was evaluated; a) review phase and reagents of instrument grade, b) expert phase comparison scores c) phase review or a signature rating.

Participants were interviewed and surveyed at the university facilities. Access to the respondents was made from the database of the medical school, considering the system of professional practices and social service of the university and the collaborating institutions. They were informed that the results of the research would not affect their school situation either positively or negatively. They were asked to answer questions and statements honestly. They were invited to consult the results in the final report of the research group. The data were processed in the statistical package for social sciences (SPSS) and the structural moment analysis software (AMOS) in versions 10 and 6.0.

The instrument was validated with 100 students from the same institution. After establishing the internal consistency that reached a value higher than the essential minimum (alpha of .805) and the ten factors, its factorial structure was confirmed with 100 interns and professional servers.

Analysis. Cronbach's alpha parameters, KMO coefficients, Bartlett's test, factor weights, Pearson correlations, covariance weights "phi", "beta" and "Rank", as well as adjustment and residual indices were used to contrast the specified relationship model with the observed data.

A kurtosis value close to unity was assumed as evidence of the normal distribution of the respondents' responses with respect to the statements that measure the study variables in an instrument with response options and interval measurement levels.

The KMO coefficients are greater than 0.600 and the Bartlett test was assumed with a significance level of less than 0.050 as evidence of product-moment correlations that facilitated the exploratory factor analysis of principal axes with promax rotation. Subsequently, factor weights greater than 0.300 were considered as evidence of variance maximization based on the factors derived from the exploratory analysis. Percentages of explained variance greater than 0.20 were assumed as evidence of acceptance of the null hypothesis.

A Cronbach's alpha value greater than 0.60 was assumed to be sufficient to demonstrate the internal consistency of the indicators with respect to the general scale and the subscales. Product-time correlation greater than 0.90 was considered as evidence of collinearity and multicollinearity, which means that the elements are similar in terms of their content.

Pearson r values ​​close to unity and zero were discarded from further analysis because they represent collinear or falsified relationships. On the other hand, those values ​​greater than 0.30 and less than 0.90 were assumed as evidence of dependency relationships.

“Phi” values ​​between 0.30 and 0.90 were identified as evidence of dependency relationships in the case of categorical variables or in combination with continuous variables.

Beta values ​​between exogenous and endogenous variables between 0.30 and 0.90 were assumed to be evidence of dependency relationships. Similarly, gamma values ​​between endogenous variables close to zero or one were discarded from further analysis.

A Goodness of Fit Index (GFI) close to one was assumed to be evidence of fit and acceptance of the null hypothesis. On the contrary, values ​​below 0.975 were considered as evidence of rejection of the null hypothesis and acceptance of the alternative hypotheses.

Values ​​close to zero were assumed to be evidence of fit between the specified relationships and the data obtained, so the null hypothesis of fit between both models was accepted. On the contrary, values ​​higher than 0.007 were considered as evidence of rejection of the null hypothesis.

Results

The values ​​that indicate the reliability and validity of the instrument that measures treatment adherence reached values ​​higher than the essential minimums of .60 and .300 respectively. The essential reliability and validity values for the analysis of the instrument's consistency and its measurement from the convergence of responses to the items. The results indicate that the instrument is consistent in its measurement of the dimensions established in the state of the art, as well as in the registration of responses to the items. That is, the reliability and validity values ​​suggest that the instrument meets the prerequisites for more detailed and specific analyzes such as sphericity and the suitability of the subscales to the study sample.

Adequacy     and     Sphericity     ⌠ X  2    = 435.12 (245     Gl     ) p = 0.000; KMO = 0.567⌡Method:     Axes    Main    ,     Rotation    : Promax: Standards (16% of the     variance     total     Explained     and alpha = 0.724), Values (13% of the     variance     total     Explained     and alpha = 0.789),     Beliefs     (10% of the     variance     total     Explained     and alpha = 0.761),     Perceptions     (7% of the     variance     total     Explained     and alpha = 0.829),     Behaviour     (5% of the     variance     total     Explained     and alpha = 0. 895). The     five    Factors    Explain    the     62% of the  varianza de la estructura.

Ten factors prevail in adherence to treatment. In other words, the orthogonality of the structure suggests that it is a multiple and diverse phenomenon, not subject to a universal protocol. Once the validity structure was established, the relationship structure was estimated considering the correlations between the factors, as well as the covariances to observe the incidence of other factors not included in the model. That is, the correlation and covariance values suggest that nine variables are determinants of treatment adherence behavior. This is because the process of adherence to immunization through vaccines involves at least ten variables in decision-making, acceptance, and application of the vaccine.

In a relationship of trust between rulers and governed that translates into co-management (p < .01; ** p < .001; *** p < .0001). The literature shares adherence to treatment to explain the deliberate, planned, and systematic social and family support that underpins the decision to carry out medical recommendations. To establish the axes, trajectories and relationships between the variables, we proceeded to estimate the effects of the deliberate process on the decision and action of adherence to treatment in a structural equation model. In other words, the structure of relationships between factors and indicators suggests that adherence to treatment is a complex phenomenon of trust between authorities and respondents. It is an immunization process that prioritizes the vaccination of students because it considers them strategic to mitigate the pandemic. In this administrative and institutional sequence, adherence to treatment is critical to reversing information that vaccines are unsafe.

The analysis of the mediating factor suggests the reduction or increase of the effect of the determinants on the dependent variability.  The results show that emotional regulation skills reduce the impact of discrimination and social frustration, as well as STPS and frustration, as well as exposure to violence. In the case of social support and support networks, this variable increases the impact of exposure to violence, discrimination and social exclusion, as well as stress and frustration. 

The parameters of adjustment and residuals [ χ ² = 16.35 (17gl) p = 0.000; GFI = 0.925; CFI = 0.975; NFI = 0.975; RMSEA = 0.003; TMR = 0.002; R ² = 0.576] suggest the non-expulsion of the null hypothesis regarding the significant differences between the structure of adherence to treatment reported in the literature with respect to the structural equation model established in this work. The adjustment of the theoretical structure with respect to the proposed model suggests that the respondents are in an organized public health sector to reduce the effects of information that refers to vaccine inefficiency.                       

Discussion

In reference to studies that conceptualize treatment adherence as independent of decision-making and its cognitive determinants, the present study has shown that they are rather part of a multifactorial process. Although cognitive factors are determinants of decisional and behavioral factors, adherence is considered an integral phenomenon where each factor affects another to structure an individual or group response to an illness or accident.

The literature on violence in the university environment encompasses various forms of violence, including dating violence, gender-based violence, and emotional violence (ESPINOZA-GÓMEZ et al., 2010). The association is significant between drug use and being a victim of psychological and physical violence among university students. The Impact of Sexual and Physical/Verbal Violence on College Students' Academic Performance uses bivariate analysis to examine the relationship with grade point average and school dropout. The trajectories of dating violence observe stable patterns in participation in the study and differences by sexual minority and gender condition.

Gender-based violence in the university context highlights the denial and struggles faced by women to file complaints with the authorities (Delgado, 2015). Gender-based violence among university students emphasizes the importance of understanding and addressing this problem. A campus-wide approach to addressing sexual violence focusing on policy development, support, and prevention efforts involve staff and students. The level of exposure to emotional violence among university students highlights the importance of understanding demographic characteristics in relation to violence.

The trend towards violence and attitudes towards domestic violence among health sciences students emphasise the need to address the factors that influence these attitudes (AL-MOMANI & RABABA, 2023). Administrators' responses to sexual violence explain the disconnect between university efforts and reports of institutional betrayal by survivors. The prevalence of dating violence patterns and risk factors among college students sheds light on the importance of addressing this issue within tertiary institutions.

GARCIA LIRIOS, (2022) validated the Medicinal Beliefs Questionnaire and the Medication Appropriation Scale, found beliefs as predictors of treatment adherence (, 90). In the present study, the ten dimensions included beliefs as an indicative factor of adherence, considering that it is a process prior to a disease and its treatment that would affect treatment behavior. In this sense, the pandemic environment would modify the relationship between general beliefs and specific self-care behavior in distancing and confinement, as well as interactive when requesting medication, supply, and application of medication.

GARCIA LIRIOS (2021c) demonstrated the negative effect of adherence to treatment on the use of antiretrovirals based on distancing and confinement of people, mediated by social, family, and institutional support. In the present work, it was shown that the ten dimensions explain 62% of the total variance, but the inclusion of the frequency of drug use and the environment of its use would allow to increase the percentage of variance explained. A second-order factor analysis will allow the first-order factors to be regrouped and to accommodate other subscales that correlate with the other subscales and indicators.

GARCIA LIRIOS (2021d) measured the knowledge, attitudes, and practices of treatment adherence in the Covid-19 era. They demonstrated contingent relationships between the three variables with respect to Covid-19 prevention. In the present study, it was observed that the three variables are associated with prevention and immunization, forming a second-order construct, although knowledge and attitudes could be modeled as determinants of practices.

Lines of research related to the conception of adherence as a second-order factor, reflected in first-order cognitive and behavioral factors, will allow anticipating rehabilitation scenarios from social, family, individual and cognitive support. The confirmatory factor analysis of a structure governed by a second-order factor will explain the influence of the environment mediated by evaluative, normative, and cognitive factors. attitudinal, motivational and decisional.

Conclusion

The structure of adherence to treatment reported in the literature shows the importance of quality of life, socioeconomic variables, and reactions to medical treatment as determinants, but in the present study it was shown that these variables are mediators of social life and group norms regarding yes or no. not adhering to treatment.

In the present work, a deliberate, planned and systematic sequence of variables related to social and family support has been established that, when interacting with cognitive variables, suppose a stable structure of adherence to treatment. S and found a structure of 10 factors around which adherence to treatment was reflected through a sequence based on the norm of conduct. In other words, adherence to treatment involves a series of principles that guide behavior, mediated by social, family, and cognitive factors. Adherence to treatment is reflected in the structure of social, family and cognitive resources without which the sequence of adherence to treatment would be impossible.

The lines of research on treatment adherence as a result of the mediation of cognitive factors from the social and socioeconomic dimensions will allow us to anticipate the barriers that inhibit it. As for the logical sequence of deliberation, planning and systematization, it is necessary to point out the sociodemographic variables that accentuate barriers or adherence to treatment.

It is suggested to perform opinion mining, the Delphi method and the symptom technique that allowed the construction of the instrument, as well as the content validity. If internal consistency refers to the degree of understanding of the content of each item, the three phases of information processing are recommended.

Therefore, the empirical testing of the model in other scenarios and samples will allow a diagnosis of the trust of health service users towards their health authorities and politicians with respect to the management of the pandemic, as well as the immunization of the population, mainly the application of vaccines to counteract the non-scientific information disseminated in the media and electronic networks.