Health and Nursing Education Models and Theories
Selected Models and Theories In Nursing Education
Compliance, adherence, and motivation are concepts germane to health behaviors of the learner. The nurse as educator focuses on health education as well as the expected health behaviors. Health behavior frameworks are blue- prints and, as such, serve as tools for the nurse as educator that can be used to maintain desired patient behaviors or promote changes (Syx, 2008).
As a result, a familiarity with models and theories that describe, explain, or predict health behaviors can increase the range of health promoting strategies for the nurse as educator. When the educator understands these frameworks, the principles inherent in each can be used either to promote compliance with a health regimen or to facilitate motivation.
This chapter presents an overview of the following models and theories: health belief model, health promotion model, self-efficacy theory, protection motivation theory, stages of change model, theory of reasoned action and theory of planned behavior, and therapeutic alliance model.
Health Belief Model In Nursing Education
The original health belief model (HBM) was developed in the 1950s from a social psychology perspective to examine why people did not participate in health screening programs (Rosenstock, 1974). This model was modified by Becker et al. (1974) to address compliance with therapeutic regimens. According to Verlander et al. (2016), the Health Belief Model frequently guides the development of interventions related to health.
The HBM explains and predicts health behaviors based on the patients’ beliefs about the health problem and the health behavior. The model relies on the assumptions that patients are willing to participate and that they believe that health is highly valued (Becker, 1990).
Direction and Flow Model Components
Both these premises need to be present for the model to be relevant in explaining health behavior. This model is grounded on the supposition that it is possible to predict health behavior given three major interacting components: individual perceptions, modifying factors, and likelihood of action. The direction and flow of these components, each of which is further divided into sub components:
1. Individual perceptions: These include the sub components of perceived susceptibility or perceived severity of a specific disease.
2. Modifying factors: These include the demographic variables (age, sex, race, ethnicity), socio psychological variables (personality, locus of control, social class, peer and reference group pressure), and structural variables (knowledge about and prior contact with disease).
These variables, in conjunction with cues to action (mass media, advice, re- minders, illness, reading material). influence the sub component of perceived threat of the specific disease.
3. Likelihood of action: This includes the sub components of perceived benefits of preventive action minus perceived barriers to preventive action.
These components and sub components are directed toward the likelihood of taking recommended preventive health action as the final phase of the model. In sum, individual perceptions and modifying factors interact. An individual appraisal of the preventive action occurs, which is followed by a prediction of the likelihood of action.
The HBM has been the predominant explanatory model since the 1970s for explaining differences in preventive health behaviors as well as use of preventive health services (Langlie, 1977). It stands out as one of the most frequently cited and researched psychosocial models to deter- mine health-related screening behavior (Wong et al., 2013).
It has been used widely in health behavior research across disciplines such as medicine, psychology, social behavior, and gerontology to predict preventive health behavior and to explain sick-role behavior in acute and chronic illnesses.
It has even been used to explain nursing students’ willingness to seek treatment for test anxiety (Markman, Balik, Braunstein- Bercovitz, & Ehrenfeld, 2011) and in evaluating the impact of an H1N1 flu vaccine initiative (Jones et al., 2015).
Over time, research studies have supported the validity of the HBM. For instance, Jacana and Forbes-Thompson (2005) studied health belief constructs in an assisted living facility and found that healthcare providers can in- fluence health beliefs relative to osteoporosis, which has implications for gerontology nursing education.
In China, Wang et al. (2013) successfully used a nursing intervention based on the HBM to enhance patients’ health beliefs and self-efficacy toward the disease management of COPD. Turner, Kivlahan, Sloan, and Haselkorn (2007) found that this model was effective in predicting adherence to a medication regimen among patients with multiple sclerosis.
Saunders, Frederick, Silverman, and Papesh (2013) determined the HBM provided an appropriate framework for examining hearing behaviors. Johnson. Mues, Mayne, and Kiblawi (2008) emphasized the need for culturally relevant screening strategies as a response to the significance of sociocultural factors influencing health related beliefs and use of healthcare services.
Findings from studies such as these, as well as from additional studies (Adams, Hall. & Fulghum, 2014; Baghianimoghadam et al. 2013: Griffin, 2011; Scarinci, Bandura, Hidalgo, & Cherrington, 2012), can be operationalized through educational programs specific to high risk populations. In a historical 10-year review of the HBM literature, Janz and Becker (1984) found that the model strongly predicted health behaviors, with perceived barriers being the most influential factor.
As it applies today. the nurse as educator needs to take into consideration the availability of barrier free educational resources. In this technology-driven society, Dutta-Bergman (2004) suggests a relationship between health beliefs, information seeking, and active versus passive learners with implications for type of health education delivery. Health educators, this author suggests, need to be concerned with consumer health seeking behaviors in the technology age.
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