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Introduction
Psychological studies of occupational health have noted: a) the preponderance of the Job Demands-Control-Support (DCS) Model and Effort-Reward Imbalance (ERI) Model; b) the prevalence of stress due to asymmetries between demands and self-control, as well as an imbalance between efforts and rewards; c) once the worker has become ill because of work stress, treatment adherence emerges as a factor of quality of life and subjective well-being 1.
Occupational health models warn that stress can affect biomedical factors; cardiovascular, cerebrovascular, and ischemic heart disease can lead to musculoskeletal disorders, absenteeism, accidents, conflicts, insomnia, depression, and anxiety 2. Both DCS and ERI have shown that the type of employment determines occupational health and selff-care. An increase in risk factors exacerbates the likelihood of work-related illnesses, accidents, or disorders 3.
However, occupational health studies have focused on prevention rather than on adherence to the treatment of diseases, accidents, or disorders since workers can rehabilitate and become productive again. It is necessary to explore treatment adherence as a determining factor in the quality of life of workers with some disease, especially the elderly or those terminally ill or in the case of the imminent loss of a limb or even life 4.
In this sense, the study of treatment adherence also involves researching grief factors in the face of an imminent loss of a limb or a life in organizations that operate at high risk with adverse health effects 5. Models that analyze grief as an expectation of the imminent loss of life, limbs, or sanity comprise phases ranging from denial to acceptance, rehabilitation, and reconstruction of the meaning of existence 6.
Treatment adherence, unlike the process of mourning, implies a hope for the preservation of the quality of life, an expectation of well-being, and indicates the restoration of occupational health 7. Its study links an accident, illness, or disorder to the convalescent reinsertion into a climate of tasks and relationships based on trust, commitment, satisfaction, entrepreneurship, innovation, and competitiveness. However, treatment adherence involves an internal negotiation of the employee with the demands that organizations will endorse; establishing agreements and responsibilities between workers and leaders is not always feasible in traditional organizational cultures, but in adhocracies 8. Therefore, this paper aims to establish the categories for the study of treatment adherence from the literature indexed to international repositories and published between 2015-2019.
This paper presents secondary research conducted using a selection of indexed sources in Latin American repositories-Dialnet, Latin-dex, Publindex, Redalyc, and Scielo-, considering the keywords "model," "demands," "control," "social support," "imbalance," "effort," and "reward." The information was processed in matrices of content analysis to extract the explanatory variables of treatment adherence. Then, the model was specified based on assumed variable dependence relationship trajectories. The model will allow for the empirical contrast of hypotheses and a new specification of variable correlation trajectories to incorporate literature findings and questions of state of the art.
Job Demands-Control-Support (Dcs) Model
Treatment adherence, from the DCS, results from the asymmetries between work demands and subjective capacity control 9, i.e., it responds to the external demands of treating a disease and personal resources. Thus, treatment adherence refers to tangible and intangible opportunities for rehabilitation or healing that can also trigger conflicts of relationships, disinterest, frustration, exhaustion, depersonalization, or neglect 10.
If significant differences prevail between treatment adherence decision-makers and those who execute them 11, treatment idealization will produce more inspiration and satisfaction 12. Commitment, as an indicator of self-control, increases treatment adherence. Therefore, the demands external to treatment are created by the patient's degree of commitment and self-regulated control regarding the idealization of their work, leaders, and organization 13.
The DCS warns that the organization can motivate the employee in a climate of trust-based relationships and innovative tasks, but the employee's work history will determine their degree of self-control reflected in their commitment and satisfaction with their work environment 14. Then, the DCS does not explain the work culture's effect on the weighted worker's performance and effort for and commitment to treatment adherence.
Effort-Reward Imbalance (ERI) Model
Both models explain social and organizational support as a predictor of treatment adherence. Unlike the DCS that emphasizes the importance of regulating demands and fostering personal control, the ERI maintains that reward, coupled with effort, will create a climate of transparent and reliable relationships, fostering the task climate, reducing conflicts to the minimum, and encouraging treatment adherence 15.
In the DCS, increased demand affects self-control, and such asymmetry favors the diseases, accidents, conflicts, and disorders that the ERI intends to solve with incentives of more significant effort since it implies a constant increase in demands. However, occupational health studies warn that extrinsic causes these emerge even in stressful work climates since the lack of personal satisfaction is a risk factor for employees with expectations and transcendent abilities 16.
Emotional exhaustion is caused by excessive work demand and the routine or simplicity of the task climate. Also associated with depersonalization, emotional exhaustion is a process that begins with fatigue and culminates with indifference to the workplace. It can even result in frustration, an inhibitor of organizational social support for adherence to occupational disease treatment 17. Therefore, the organizational social support, flexibility, trust, and diversity necessary to relieve stress and exhaustion are reduced by the conflict of relationships and tasks 18.
The climate of relationships determines collaborative performance 19. This organizational social support predictor of adherence to occupational disease treatment is a function of knowledge dissemination 20. Therefore, the effectiveness of expected results 21 affects the quality of work relations and the avoidance of stress and exhaustion 22.
Treatment adherence studies on work demands and self-control
This section presents the studies that report correlation or regression coefficients in the relationship between work demands and treatment adherence through self-control. In these models, various variables interact, but a linear relationship prevails among them.
Self-control, defined as inconsistency in medication intake, determined treatment adherence because patients suffered psychological burdens that affected their treatment and influenced the deterioration of their quality of life 23. The determining factor of adherence was self-management (β = 0.15, p = 0.002). Organizations that do not monitor the psychological burdens of employees with high-risk functions, nor promote relationships of collaboration, support, and solidarity, seem to reduce the management of their health, causing non-adherence to the treatment of a disease or accident.
Treatment adherence determines rehabilitation. In contrasting the model, the effect of treatment cost on adherence was demonstrated (β = 0.610, p = 0.0791, OR = 0.54 (0.21-1.40)) 24. Simply put, self-management related to labor, financial, and family support reflects effective human capital against health risks and threats in the environment. Therefore, training in occupational health for human capital entails accident and disease prevention and health promotion through self-care. Rehabilitation as a quality indicator of health care would be associated with treatment adherence as an indicator of self-care.
The concept of treatment adherence as a factor determined by medication financing complements training in self-care for human capital 25. Treatment adherence seems to derive from a structure that favors a sector formed as human capital with high self-control, compared to other sectors formed as social capital with strong solidarity and collaboration ties.
The studies described above reveal that the structure of public health services and the structure of academic, professional, and work training to favor a civil sector determine adherence to the treatment of diseases and the prevention of risks by promoting healthy lifestyles 26. This self-control training policy strategy complements treatment adherence with psychological variables such as beliefs or information processing capacity, perceptions or biases, knowledge or logic of verifiability, and intentions or probabilities of deciding.
Through such a process, the Dcs and ERI turn out to be palliative institutional strategies for illnesses and accidents. Collective self-control in the first model and personal self-control in the second model are insufficient to reduce the mediation of a public health system favorable to a sector with high incomes and quality of life estimated based on urban services 27.
For self-control, a treatment adherence model proposal would be based on public management and social and family support. It would include self-care-oriented health prevention and promotion strategies 28. The purpose of this new model would be to establish a balance between the demands of the environment and institutional and personal resources. The psychological variables of beliefs, attitudes, knowledge, and intentions would be influenced by environmental demands or health risks, such as occupational diseases and accidents. The emergence of a condition gives rise to medical consultation or treatment adherence through drug intake and encourages financial and social support strategies that the State could implement according to the level of development of the community or locality 29.
In this way, treatment adherence would no longer result from urban health policies that favor those who have decision-making power centered on their personal and financial resources. It would derive from policies according to communities' needs and expectations where the formation of solidarity capital and the supporting climate would prevail over community transmitted diseases such as epidemics or pandemics 30. The State would cease to be a self-management and self-control promoter only. Its new social function would be to micro-finance health services as human settlements move away from urban centers and other non-favored sectors approach this sub-scheme to deal with their illnesses and accidents 31. Therefore, health services show a social side because of the administration scheme and the targeting of needs and support for marginalized, excluded, and violated sectors.
Specification of the treatment adherence model
The public health models used, DCS and ERI, can not only be complementary in predicting stress. If they include treatment adherence after accidents, diseases, disorders, or conflicts arising from the climate of relationships and tasks, they promote trust, commitment, entrepreneurship, innovation, and satisfaction 32.
The proposed hybrid model includes four explanatory hypotheses of variable correlation trajectories reviewed in the literature 33. The association between demands and social support determines self-control and effort (Hypotheses 1 and 2). As the worker is involved in greater demands for efficiency and effectiveness, the social support of colleagues, friends, or family encourages self-control and regulates their effort. However, if the latter is not even recognized, then it will cause stress. Otherwise, the reward could anticipate the worker's adherence to the treatment of a disease, disorder, accident, or conflict (Hypotheses 3 and 4).
Unlike predecessors, the specified model states that dependency relationships among variables anticipate stress and treatment adherence. It would also encourage the follow-up on accidents, illnesses, conflicts, and disorders associated with the climate of relationships and tasks.
Method
The new model assumes the relationship between socioeconomic variables (per capita income), demographic factors (gender, age), institutional factors (micro-financing), and psychological strategies (attitudes, knowledge, and intentions. It is then necessary to carry out an exploratory study of the relationships reported between these variables to establish the parameters that indicate probabilities of treatment adherence.
The data universe was indexed repositories such as Copernicus, Dialnet, Latindex, Publindex, Redalyc, and Scielo. The sample consisted of the literature selected intentionally with the keywords "treatment adherence," "self-control," and "work demands," and published between 2005-2019 (see Table 1).
Table 1 Description of the data sample
| Treatment Adherence | Self-control | Work Demands | |
|---|---|---|---|
| Dialnet | 35 | 30 | 26 |
| Latindex | 27 | 24 | 21 |
| Publindex | 21 | 20 | 13 |
| Redalyc | 17 | 16 | 9 |
| Scielo | 10 | 11 | 4 |
Note. N = 38
Source: Own elaboration.
In order to weigh the findings reported by the reviewed literature, they were categorized according to the relationships between organizational social support and treatment adherence, as follows: Type A (literature with positive and significant results); Type B (literature with positive and spurious findings); Type c (literature with results of null relationships between organizational social support and treatment adherence); Type D (literature with negative results), and Type E literature with findings of unknown but theorized relationships) (see Table 2).
Table 2 Description of the data sample according to the type of literature
| A | B | C | D | E | |
|---|---|---|---|---|---|
| Dialnet | 10 | 7 | 5 | 3 | 2 |
| Latindex | 7 | 5 | 3 | 2 | 1 |
| Publindex | 4 | 3 | 2 | 1 | 0 |
| Redalyc | 2 | 2 | 1 | 0 | 0 |
| Scielo | 1 | 1 | 0 | 0 | 0 |
Note. Type A (el = significant relationships between organizational social support and treatment adherence), Type B (e2 = spurious relationships between organizational social support and treatment adherence), Type c (e3 = null relationships between organizational social support and treatment adherence), Type D (e4 = negative relationships between organizational social support and treatment adherence), Type E (e5 = unknown relationships between organizational social support and treatment adherence).
Source: Own elaboration.
The information was processed following the Delphi technique. Pairs of subject-matter experts evaluated extracts of the findings reported in the literature to establish the relationship among variables, considering -1 for negative data, 0 for irrelevant data, 1 for positive data (see Table 3).
Table 3 Delphi technique
Source: Own elaboration.
In three rounds, the experts graded the extracts. First, they only compared the data. In the second phase, they gave feedback on their peers' scores. Upon reaching consensus in the third round, the judges compared their first and second opinions and gave a new score to reinforce their opinion or express new considerations.
Data were estimated considering the respondent's privacy, inhibiting the distrust of the possibility of being identified 33. In other words, data are coded (Table 2) in order not to affect their analysis, ensuring the confidentiality and anonymity of sources. In the traditional standard model, the respondents reduced or amplified their responses to not be identified. However, in the local privacy model, a random response algorithm processes these data without altering their dimensions, volume, or categories to avoid inference or association of respondents to a group 34. The random response algorithm is compatible with iPhone or Google technology.
Because the chi-square parameter is highly effective for nonparametric samples, the traditional standard model injects the disturbance into the data to ensure anonymity and confidentiality. Meanwhile, the local privacy model randomly injects the disturbance to everyone's responses, developing as many models as possible, and thereby reducing measurement biases or errors 35.
The local priority technique injects Laplace or Gaussian noise to orient the responses towards a chi-square distribution and establishes the contrast of dependence hypothesis or model fitting 36. The local privacy model is relevant because it is possible from its chi-square distribution to contrast models, considering distribution independence and adjustment, and apply regression models with ordinary unweighted least-squares 37. The local privacy model also offers minimum convergence rates to show the differences between privacy and statistical efficiency 29.
Consider datasets x = (x1; …; xn) Є Xn in some data universe X; typically X = (0; 1)d where d is the dimensionality. Two datasets x; x1 Є Xn are neigh boring if they differ in at least one element, i.e., 3i Є [n] such that x1 =/= x’1 and ¥j =/= i, xj = x’j. From this nomenclature, it is possible to advance in the hypothesis test. A null hypothesis can be rejected in the traditional standard model, limiting the error of choosing a false finding but offering in conclusive hypothesis tests when designing them to avoid type 1 errors. In contrast, the local privacy model allows the design of hypothesis tests to compare null hypotheses and avoid type 2 errors or non-rejection of false findings 38.
The information was processed in the qualitative data analysis package MIX-2, version 4.0, to quantify distribution parameters and nonparametric relationships. The chi-square was estimated, and, based on it, an instrument for model specification was designed.
Results
Table 4 shows the relationships between the selected extracts for the three major categories (work demands, self-control, and treatment adherence). It is possible to observe significant relationships to the extent that grading rounds succeeded one another, and a marked consensus on treatment adherence was reached.
Table 4 Descriptive relationships between categories and information extracts
| E | M | SD | A | c1 | c2 | c3 | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| R1 | X 2 | df | P | X2 | df | P | X2 | df | P | |||
| e1 | 0.682 | 0.143 | 0.139 | 14.24 | 15 | < 0.05 | ||||||
| e2 | 0.781 | 0.120 | 0.105 | 13.25 | 14 | < 0.05 | ||||||
| e3 | 0.604 | 0.137 | 0.143 | |||||||||
| e4 | 0.740 | 0.146 | 0.125 | 16.27 | 15 | < 0.05 | ||||||
| e5 | 0.738 | 0.122 | 0.174 | |||||||||
| R2 | ||||||||||||
| e1 | 0.773 | 0.145 | 0.143 | |||||||||
| e2 | 0.762 | 0.187 | 0.196 | 15.26 | 13 | < 0.05 | ||||||
| e3 | 0.739 | 0.190 | 0.178 | 14.35 | 14 | < 0.05 | ||||||
| e4 | 0.741 | 0.163 | 0.132 | 17.29 | 15 | < 0.05 | 16.28 | 12 | < 0.05 | |||
| e5 | 0.793 | 0.132 | 0.146 | 12.12 | 13 | < 0.05 | 14.23 | 15 | < 0.05 | |||
| R3 | ||||||||||||
| e1 | 0.734 | 0.136 | 0.139 | 15.26 | 13 | < 0.05 | 15.36 | 12 | < 0.05 | |||
| e2 | 0.762 | 0.148 | 0.176 | 14.25 | 12 | < 0.05 | 16.58 | 123 | < 0.05 | |||
| e3 | 0.751 | 0.156 | 0.167 | 16.27 | 12 | < 0.05 | ||||||
| e4 | 0.780 | 0.139 | 0.109 | 11.24 | 13 | < 0.05 | 11.43 | 12 | < 0.05 | |||
| e5 | 0.763 | 0.130 | 0.146 | 15.43 | 12 | < 0.05 | ||||||
Note. E = extract; R = round, R1 = evaluation, R2 = feedback, R3 = consensus. Type A (el = significant relationships between organizational social support and treatment adherence), Type B (e2 = spurious relationships between organizational social support and treatment adherence), Type c (e3 = null relationships between organizational social support and treatment adherence), Type D (e4 = negative relationships between organizational social support and treatment adherence), Type E (e5 = new relationships between organizational social support and treatment adherence). M = mean, so = standard deviation, A = asymmetry. c = category, c1 = treatment adherence, c2 = self-control, c3 = work demands.
Source: Own elaboration.
Upon establishing the significant relationships among categories and the information extracts graded by the expert judges, we proceeded to estimate the structure of relationship trajectories (see Figure 1).
Note. E = extract; R = round, R1 = evaluation, R2 = feedback, R3 = consensus. Type A (el = significant relationships between organizational social support and treatment adherence), Type B (e2 = spurious relationships between organizational social support and treatment adherence), Type c (e3 = null relationships between organizational social support and treatment adherence), Type D (e4 = negative relationships between organizational social support and treatment adherence), Type E (e5 = new relationships between organizational social support and treatment adherence). c = category, c1 = treatment adherence, c2 = self-control, c3 = work demands. Source: Own elaboration.
Figure 1 Structure of category relationship trajectories for the information extracts
It can be appreciated that the consensus structure is weak because the relation trajectories between categories and extracts are close to zero, suggesting a spurious scenario. This result indicates that the expert judges who graded the information extracts recommended including other variables not contained in the reviewed models such as perceptions of risk, usefulness, efficacy, and treatment adherence.
Discussion
This paper has specified a determining factor model for treatment adherence from the review of the DCS and ERI. It explains the follow-up on accidents or diseases arising from conflicting work climates and their relationships with tasks. However, the model does not account for the subsequent treatment adherence processes, such as quality of life and subjective well-being.
Subjective well-being occurs when the task climate is subordinated to the relationship climate. In cases of illness or accident, treatment adherence increased subjective well-being by empowering the organization's employees. The worker evaluated their reintegration positively but contemplated other job offers with better rewards 39. So, the quality of life starts from the organizational culture and culminates with the worker's reinsertion if they limit their expectations to the organizational culture 40.
However, treatment adherence is a mediating variable between the organizational culture and the worker's well-being and implies self-care attitudes indicated by the frequency of consultations, medication intake, and attendance at therapy and rehabilitation sessions 41. It is more linked to social support than to the attitudes of injured or sick workers. In this phase, institutionalism determines the follow-up on cases. Because social security is an institutional derivative, the organizations that offer social benefits are distinguished from companies that hire their employees for limited periods as they provide a better quality of life 42. Therefore, the relationship between treatment adherence and quality of life would help explain the effects of the organizations' follow-up on their injured or sick workers 43.
The model specification would include: a) organizational culture-opportunity climate, risk climate, benefit climate-; b) capacities- attitudes, adherence, skills, knowledge-; c) biopsychosocial support-treatment quality, family support, therapeutic assistance-; d) quality of life, and e) subjective well-being-expectations, needs-.
In establishing a structure of organizational support categories for treatment adherence, this paper suggests the observation of relationships among variables such as self-control and work demands that the proposed model only explores. However, the model's confirmation in the occupational health or work psychological environment is pending. It means then that the established model gains consistent potential in the organizational diagnosis of the environmental demands against workers' resources and their effects on workers' health.
In this sense, organizations interested in balancing demands and resources encourage treatment adherence by promoting self-control in their talents. Alternatively, they reduce the risks of accidents and diseases through decision-making and responsible actions. To the extent that workers become aware of their responsibility for their health from the challenges of their environment and their abilities, organizations will implement occupational health programs focused on the climate of relationships or social work support.
In the discussion on treatment adherence, this process has been observed: a) as an effect after the diagnosis of diseases or accidents arising from an imbalance between demands and resources; b) as a consequence of social, family, and organizational support; c) as a subsequent phase to risk prevention and concomitant to self-control, but has been directly related to demands in this work.
Studies from organizational psychology and occupational health have established an indirect relationship between demands, capacities, accidents, diseases, and treatment adherence, but in this paper, we have explored the possibility that self-control is a mediating or moderating variable. A decrease or increase in self-control as a mediating or moderating variable in the relationship between organizational support and treatment adherence would explain why organizations that foster personal responsibility prevent risks of accidents or diseases.
Conclusion
This study intended to specify a model for the study of treatment adherence. Although the research design limited findings to the data sample, we suggest that research be extended to repositories such as Scopus and WoS.
Regarding health policies focused on treatment adherence because of the formation of an intellectual capital guided by self-control, it is necessary to involve the State in financing medicines and institutional and family support as pillars of risk-free lifestyles, health promotion, and diseases and accident prevention.
