Determinants of Self-Medication among Military Retirees in South-west Iran: A Protection Motivation Theory Approach

Determinants of Self-Medication among Military Retirees in South-west Iran: A Protection Motivation Theory Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of Self-Medication among Military Retirees in South-west Iran: A Protection Motivation Theory Approach Nasser Hatamzadeh, Abdolrahim Asadollahi, Fatemeh Adlirad, Samaneh Motalebi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5360936/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Self-medication is prevalent among the elderly population, posing significant health risks. In Iran, older adults frequently engage in self-medication due to various socio-economic, cultural, and health-related factors. This study aimed to examine the determinants of self-medication among older adults in Ahvaz, Iran, using the Protection Motivation Theory (PMT) framework. Method A cross-sectional study was conducted with 340 military retirees aged 60 and above in Ahvaz City. Data were collected using a structured questionnaire that gathered demographic information and assessed constructs from the Protection Motivation Theory (PMT), including self-efficacy, susceptibility, severity, response cost, and behavior. Additional factors, such as experience, barriers, and resonators, were also evaluated. Data analysis was performed using SPSS, with structural equation modeling conducted in JAMOVI. Results The mean age of participants was 67.05 years (SD = 7.00), with 72.4% males and 78.8% married individuals. Education was significantly correlated with self-medication prevention behaviors (ANOVA, p = 0.002), where higher education levels promoted more responsible medication management. Among PMT variables, response efficacy (mean = 17.06, SD = 3.66) and severity (mean = 6.72, SD = 1.90) showed the highest influence on behavior. The study also revealed a significant positive correlation between behavior and barriers (r = 0.536, p < 0.05), resonator (r = 0.692, p < 0.05), and response effectiveness (r = 0.45, p < 0.05). Gender differences were observed, with men scoring higher in perceived severity (p = 0.001, Cohen’s d = 0.432) and resonators (p < 0.001, Cohen’s d = 0.442). Furthermore, experience (p < 0.001, Cohen’s d = 0.443) was a significant factor affecting self-medication prevention behaviors. Conclusion Education and gender play a crucial role in shaping self-medication prevention behaviors among older adults. Higher education levels corresponded to better medication management, while gender-specific differences were evident in key PMT constructs. These findings provide critical insights for developing targeted interventions to promote safe self-medication practices and improve healthcare outcomes for older adults. self-medication elderly Protection Motivation Theory education gender Iran healthcare interventions Figures Figure 1 Figure 2 Introduction Medication plays a crucial role in treating diseases, but its arbitrary use can lead to negative effects and fail to address the underlying condition. While society often views medication as inherently safe and healing, careless consumption can result in adverse side effects. Self-care, which sometimes manifests as self-medication, is different from professional healthcare. Self-medication involves treating one's ailments with medication without professional consultation or guidance, using products whether herbal or synthetic for treatment, symptom relief, prevention, and health improvement without a prescription [ 1 , 2 ]. Arbitrary self-medication includes behaviors such as obtaining pharmaceuticals without a prescription, reusing previously prescribed medications for similar ailments, sharing prescribed drugs with others, and using leftover medications. This spectrum ranges from neglecting an ailment to seeking medical assistance, including self-medication[ 3 ]. Currently, arbitrary drug use presents a significant challenge in the healthcare system of Iran and many other countries. This trend has led to bacterial resistance, inappropriate treatment outcomes, accidental poisonings, adverse side effects, disruptions in the pharmaceutical market, wasteful spending, and increased per capita drug expenditure[ 2 ]. In Iran, the prevalence of self-medication varies from 12–90%, with each Iranian consuming around 339 drugs per year, exceeding the global average [ 4 ]. The elderly, who are more susceptible to diseases, may engage in self-medication due to their health status [ 5 ]. Biochemical, physiological, and pharmacokinetic changes in the Older People make them prone to excessive or incorrect use of medication. Therefore, addressing self-medication concerns among the Older People is particularly important, especially considering the expanding availability of medications in this age group [ 6 ]. Aging is a critical phase of life requiring attention to associated challenges and needs. Research highlights the vulnerability of the Older People to arbitrary medication use, emphasizing the need for targeted interventions due to their higher risk compared to other age groups. The prevalence of comorbidity among the Older People leads to increased medication use, further increasing their susceptibility to drug-related complications. Given the prevalence and negative effects of self-medication, it is clear that the Older People bear a disproportionate burden of medical expenses, with doctor visits and medical costs exceeding 60% for this age group [ 6 ]. Therefore, with the growing Older People population, it is crucial for healthcare services to tailor their offerings to address the unique needs of this demographic. This requires a comprehensive understanding of the prevalence and factors that influence arbitrary drug use in society [ 7 ]. Choosing a suitable framework is of utmost importance in healthcare planning [ 8 , 9 ] The Protection Motivation Theory emerges as one of the most effective theories in promoting preventive behaviors. With its emphasis on motivation and skill development, this theory highlights the significance of fostering adaptive responses to threats [ 10 ]. Consequently, this study assesses the influential factors that contribute to the prevention of self-medication among the Older People in Ahvaz city based on the Protection Motivation Theory [ 11 ]. Method Study Design and Population: This descriptive, analytical, and cross-sectional study investigated factors influencing self-medication among retired military personnel aged 60 and above, residing in Ahvaz City, Khuzestan Province, South Iran. Sampling Procedure and Sample Size Determination: A multi-stage cluster sampling approach was employed to systematically select participants. Two primary health centers located in the North Campello and Takhti areas of Ahvaz City (Health Centers No. 8 and 9) were initially chosen as clusters. Subsequently, a simple random sampling technique was applied within each center, inviting individuals aged 60 and above to participate in the study. The sample size was determined using a standard formula for estimating proportions in cross-sectional studies: $$\:n=\frac{{Z}^{2}\times\:p\times\:(1-p)}{{d}^{2}}$$ where: (Z) represents the Z-score for the 95% confidence level (1.96), (p) is the estimated prevalence of self-medication, set at 42% [ 10 ], and and (d) is the margin of error, specified as 5%. Applying this formula with a prevalence estimate of 42% yielded an adequate sample size to ensure reliable data collection for assessing self-medication behaviors. During data collection, it was observed that a significant proportion of clients at these centers were retired military personnel. As a result, the study was refined to focus specifically on this demographic, and only data pertaining to military retirees were included in the final analysis. Inclusion and Exclusion Criteria: The study included military retirees aged 60 and above who were permanent residents of Ahvaz City and willing to participate. Individuals were excluded if they were unable to communicate effectively, had severe cognitive impairments, or declined participation. Data Collection: The data collection was conducted using a structured questionnaire comprising three distinct sections [ 11 ]. The first section focused on demographic information, including variables such as age, gender, education level, marital status, and health insurance status. In this section, participants also reported their self-medication practices over the past month. The second section of the questionnaire addressed constructs from the Protection Motivation Theory, which included susceptibility, severity, response cost, response efficacy, self-efficacy, rewards, behavior, and fear associated with self-medication. In addition to the standard constructs of the Protection Motivation Theory, three supplementary constructs were incorporated into the questionnaire. The first of these, "Experience," assessed participants' previous engagement in self-medication, including the frequency of such behavior and the outcomes, particularly symptom improvement. The "Barriers" construct explored factors that contribute to self-medication, encompassing eight items such as financial constraints, lack of health insurance, overcrowded medical centers, high medical costs, and long distances to healthcare facilities. Lastly, the "Reasons" construct examined the motivations behind self-medication, featuring ten items that included the use of medication without a doctor's prescription for conditions such as back pain. Statistical Analysis: Data were analyzed using SPSS version 21, with descriptive statistics such as frequencies, percentages, means, and standard deviations computed to summarize demographic variables. Pearson's correlation tests explored relationships between variables, while logistic regression analysis identified factors associated with self-medication among the elderly. A significance level of 0.05 was applied for all statistical tests. Additionally, structural equation modeling using the Maximum Likelihood method was conducted in JAMOVI v.2.4.5 to develop models elucidating the underlying mechanisms within the PMT framework [ 12 , 13 ] Ethical Approval: This study received ethical approval from Ahwaz University of Medical Sciences, with the code IR.AJUMS.REC.1402.494, on December 16th, 2023. The study adhered to the COPE-2009 guidelines, the Helsinki Convention 2020 rules, and its 2022 amendment to ensure confidentiality and anonymity. Results Participant Profile: A total of 340 military retirees participated, with an average age of 65.07 ± 7.00 years. Of the participants, 246 (72.4%) were male, while 94 (27.6%) were female. The majority were married (78.8%), and 40.9% had a diploma-level education. A notable correlation was observed between education level and self-medication prevention behaviors, particularly among those with a college education compared to those with primary, secondary, and diploma education levels (Table 1). Protection Motivation Theory Structures: Table 2 displays descriptive statistics for each PMT variable related to self-medication prevention behaviors. Variables with the highest scores included Cause (19.18 ± 7.19), Response Efficiency (17.06 ± 3.66), and Barriers (13.55 ± 2.54), while those with the lowest scores were Resonator (4.65 ± 1.02), Reward (5.29 ± 2.02), and Susceptibility (5.79 ± 2.06). Correlation Matrix: The correlation matrix (Table 3) revealed significant associations between self-medication prevention behaviors and PMT constructs, with the strongest correlation identified between Cause and self-medication prevention behaviors. Gender Comparison Analysis: An independent t-test comparing Older People men and women revealed significant differences in intensity and experience variables (P < 0.001), with Cohen's d effect size exceeding 0.4. These results suggest that gender significantly influences the PMT model for these variables. A violin plot (Figure 1) illustrated the distribution of intensity and experience scores between genders, indicating higher scores among men. Structural Equation Modeling: Figure 2 depicts six conceptual models developed using the Maximum Likelihood method of structural equation modeling on the main PMT components. The two models with the highest fit indices (GFI & CFI > 0.9) and the lowest estimation error (RMSR & RMSEA < 0.07) based on the theoretical PMT model for interpreting drug abuse behavior in the Older People population of Khuzestan were selected. In Model 1, the causal component (CAUS) had an effect coefficient of over 60% on the behavior variable (BHV), and the becoming component (SEV) had a significant mediating effect of 43% on the fear mediator component (P < 0.001). In Model 2, the causal component (CAUS) again had a significant effect coefficient of over 65% on the behavior variable. The fear component (FR) had a predictive power of 34% on the reward component (RWRD), explaining 10% of the variance in the behavior variable (P < 0.01). Discussion This study investigated self-medication practices among military retirees in Ahvaz City, a group characterized predominantly by married males with diverse educational backgrounds. The analysis revealed a significant correlation between education level and self-medication prevention behaviors, underscoring the crucial role of education in shaping health-related decision-making among retired military personnel. This finding aligns with previous research showing that individuals with higher education levels tend to be more aware of the risks associated with self-medication, fostering a more cautious approach to medication use [14, 15]. Emphasizing education within this demographic could enhance understanding of the risks and consequences of self-medication, promoting safer practices among retired military individuals. This insight is particularly valuable for designing targeted educational interventions aimed at improving medication management and health outcomes among military retirees. The study applied the Protection Motivation Theory (PMT) framework, examining constructs such as self-efficacy, rewards, fear, response efficacy, response cost, severity, susceptibility, and behavior in relation to self-medication prevention [10]. The findings indicated high scores in perceived severity, response efficacy, and barriers, which were essential in shaping self-medication prevention behaviors among military retirees. These results align with previous research that identifies perceived severity and response efficacy as key factors influencing self-medication decisions [16, 17]. Additionally, gender differences were observed, with male participants reporting varying experiences and intensities in these PMT constructs. This suggests that gender-specific considerations are critical in addressing health-related behaviors among military retirees, highlighting the importance of developing gender-sensitive programs to promote responsible medication use and improve overall health outcomes in this demographic [17]. In addition to core PMT constructs, this study incorporated supplementary factors such as experience, barriers, and resonator effects. The "Experience" construct offered insights into previous self-medication episodes among military retirees, while "Barriers" encompassed obstacles like financial constraints and healthcare accessibility issues factors that have been consistently identified in literature as determinants of self-medication [15]. The "Resonator" construct, though less validated, pointed to potential mediators that influence self-medication behaviors. Further research is warranted to fully understand the role of this construct in modulating self-medication within military populations. This study expands on existing literature by focusing specifically on military retirees and providing a more comprehensive understanding of the psychological and socio-economic determinants that shape self-medication behaviors in this unique group. The findings are consistent with previous studies on self-medication among older populations, showing that education, chronic illness, and cultural attitudes are significant predictors of self-medication [18]. By incorporating the PMT framework, this study offers a nuanced perspective on how motivational factors affect self-medication decisions among military retirees, distinguishing this group from the general elderly population. While this study’s strengths include its use of a large sample size and structural equation modeling to examine self-medication behaviors, the cross-sectional design limits causal inference. The reliance on self-reported data also introduces potential recall bias, and the focus on military retirees in Ahvaz City may limit generalizability to other populations [19, 20]. Future research could benefit from longitudinal studies to assess changes in self-medication behaviors over time, as well as qualitative approaches to explore cultural and contextual influences. Expanding this research to other regions and including diverse military retiree populations could enhance understanding of self-medication behaviors across different contexts. Conclusion In conclusion, this study provides valuable insights into self-medication practices among military retirees in Ahvaz City, emphasizing the importance of education as a key determinant in preventing self-medication behaviors. The PMT framework, complemented by additional constructs, identified significant motivational factors such as perceived severity, response efficacy, and barriers that influence self-medication practices in this demographic. Observed gender differences suggest a need for tailored, gender-sensitive interventions to promote safe medication use. These findings contribute to the growing body of research on self-medication among military retirees and highlight the need for targeted strategies to improve healthcare practices for this specific group. Declarations E thics approval and consent to participate This study received ethical approval from Ahvaz Jundishapur University of Medical Sciences (Ethics code: IR.AJUMS.REC.1402.494, approved on December 16, 2023). Informed consent was obtained from all participants, ensuring their voluntary involvement and confidentiality. Consent for publication All participants provided consent for publication of the anonymized data. C ompeting interests The authors declare no competing interests. Funding No external funding was received for this study Availability of data and materials Data supporting the findings of this study are available from the corresponding author upon reasonable request Acknowledgements The authors extend their gratitude to Ahvaz Jundishapur University of Medical Sciences for providing ethical approval and to the participants for their invaluable contributions to this research. Authors' information Details on authors’ affiliations and contributions are included in the manuscript Authors' contributions Nasser Hatamzadeh : Conceptualized the study design, led data acquisition, performed data analysis, and contributed to drafting and critically reviewing the manuscript. Abdolrahim Asadollahi : Assisted with data collection, conducted statistical analyses, and contributed to the interpretation of results and manuscript writing. Fatemeh Adlirad : Conducted the literature review, supported data collection, contributed to data organization, and assisted in preliminary data analysis. Samaneh Motalebi : Played a role in data management, contributed to data analysis, and reviewed the manuscript for intellectual content. Najmeh Yazdanparast : Participated in data collection, assisted in data interpretation, and provided critical revisions of the manuscript. Fatemeh Modhej : Participated in data collection, assisted in data interpretation, and provided critical revisions of the manuscript. Morteza Abdullatif Khafaie : Served as the corresponding author, supervised the entire research project, ensured the integrity and accuracy of the work, and provided final approval of the manuscript. References Papakosta M, Zavras D, Niakas D: Investigating factors of self-care orientation and self-medication use in a Greek rural area . Rural and remote health 2014, 14 :2349. R. Tajik MS, M. Shamsi MS, A. 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Tables Table 1 Descriptive Statistics and Correlations of Demographic Variables and Self-Medication Prevention Behaviors Variable Mean (SD)/ Frequencies (%) Behaviors Statistics P Value Age 67.05 (7.00) r = 0.09 0.08 Gender Male Female 264 (72.4%) 94 (27.6%) t = -0.84 0.40 Marital Status Single Married 72 (22.2%) 268 (78.8%) t = 0.78 0.43 Education Primary Secondary Diploma Colleges 49 (14.4%) 70 (20.6%) 139 (40.9%) 82 (24.1%) F = 6.07 0.002* * Using anova, P < 0.05 Table 2: The Structures of the Protection Motivation Theory in Self-Medication Prevention Behaviors Variables Mean (SD) Minimum-Maximum Self-efficacy 9.29) ) 2.55 3-12 Reward 5.29(2.02) 2-8 Fear 11.90(3.19) 4-16 Behavior 7.80(3.28) 4-20 Response efficiency 17.06(3.66) 5-20 Response cost 7.85(2.86) 3-12 Severity 6.72(1.90) 2-8 Susceptibility 5.79(2.06) 2-8 Other factors affecting self-medication that have been added to the protection motivation theory: experience 4.65(1.02) 3-6 Barriers 13.55(2.54) 8-16 Resonators 19.18(7.19) 10-50 Table 3: Pearson's Correlation Coefficient Matrix of Protection Motivation Theory Constructs in Understanding Drug Use among military retirees Residents in Ahvaz City Behavior Barriers Resonator experience Susceptibility Severity Response cost Response effectiveness Fear Reward Self-efficacy Self-efficacy R= -0.28 Reward r=0.33** r=0.35** Fear r=0.33** r=0.80 r=0.43** Response effectiveness r=0.11* r=0.305** r=0.45** r=-0.48 Response cost r=0.45** r=0.27** r=0.511** r=0.24** r=0.38** Severity r=0.75** r=0.41** r=0.15** r=0.36** r=0.117* r=0.48** Susceptibility r=-0.079 r=-0.01 r=0.01 r=0.22** r=-0.015 r=0.16** r=-0.22** experience r=-0.274** r=0.131* r=0.04 r=-0.24 r=-0.12* r=0.15** r=0.16** r=-0.20** Resonator r=0.536 ** r=-0.248** r=0.187** r=0.035 r=0.23** r=0.28** r=0.021 r=-0.07 r=0.27** Barriers r=-0.150** r=-0.236** r=0.692** r=-0.140** r=-0.096 r=0.02 r=0.21** r=-0.081 r=0.17** r=-0.22** Behavior (Note: ** indicates statistical significance, P < 0.05) Table 4: Gender Comparison through Independent Samples T-Test in Understanding Drug Use among military retirees Variables t-test df p Effect Size: Cohen’s d 95% CI Perceived Susceptibility 1.145 338 0.114 0.192 -0.046 0.430 Perceived Severity -0.921 338 0.001 0.432 -0.191 0.673 Response Cost -0.063 338 0.204 -0.351 -0.591 -0.111 Response Effectiveness 1.519 338 0.949 -0.007 -0.245 0.230 Self-Efficacy -2.244 338 0.130 0.184 -0.054 0.422 Reward -2.901 338 0.425 -0.272 -0.510 -0.033 Fear 0.498 338 0.358 -0.112 -0.349 0.126 Behavior -0.921 338 0.619 0.061 -0.177 0.298 Other factors affecting self-medication that have been added to the protection motivation theory: Experience -0.570 338 <0.001 0.443 0.201 0.683 Barrier 1.584 338 0.254 0.442 0.201 0.683 Resonator 3.651 338 <0.001 0.442 0.201 0.638 Additional Declarations No competing interests reported. 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FR: Fear; RWRD: Reward; BHV: Behavior; SEV: Severity\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5360936/v1/8b8d3d2842baaad88d951374.png"},{"id":104774756,"identity":"2a855330-0ae5-4fb5-8d39-5661ea65b759","added_by":"auto","created_at":"2026-03-17 06:26:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1739079,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5360936/v1/7c979d6e-26bb-480e-b1f0-66e697248fd5.pdf"},{"id":70508419,"identity":"4d66aee3-da11-4f50-a20a-27c7c7c43565","added_by":"auto","created_at":"2024-12-04 00:07:30","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1105288,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5360936/v1/af70dabf5784446a7c1fbc07.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of Self-Medication among Military Retirees in South-west Iran: A Protection Motivation Theory Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMedication plays a crucial role in treating diseases, but its arbitrary use can lead to negative effects and fail to address the underlying condition. While society often views medication as inherently safe and healing, careless consumption can result in adverse side effects. Self-care, which sometimes manifests as self-medication, is different from professional healthcare. Self-medication involves treating one's ailments with medication without professional consultation or guidance, using products whether herbal or synthetic for treatment, symptom relief, prevention, and health improvement without a prescription [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Arbitrary self-medication includes behaviors such as obtaining pharmaceuticals without a prescription, reusing previously prescribed medications for similar ailments, sharing prescribed drugs with others, and using leftover medications. This spectrum ranges from neglecting an ailment to seeking medical assistance, including self-medication[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, arbitrary drug use presents a significant challenge in the healthcare system of Iran and many other countries. This trend has led to bacterial resistance, inappropriate treatment outcomes, accidental poisonings, adverse side effects, disruptions in the pharmaceutical market, wasteful spending, and increased per capita drug expenditure[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Iran, the prevalence of self-medication varies from 12\u0026ndash;90%, with each Iranian consuming around 339 drugs per year, exceeding the global average [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The elderly, who are more susceptible to diseases, may engage in self-medication due to their health status [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Biochemical, physiological, and pharmacokinetic changes in the Older People make them prone to excessive or incorrect use of medication. Therefore, addressing self-medication concerns among the Older People is particularly important, especially considering the expanding availability of medications in this age group [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAging is a critical phase of life requiring attention to associated challenges and needs. Research highlights the vulnerability of the Older People to arbitrary medication use, emphasizing the need for targeted interventions due to their higher risk compared to other age groups. The prevalence of comorbidity among the Older People leads to increased medication use, further increasing their susceptibility to drug-related complications. Given the prevalence and negative effects of self-medication, it is clear that the Older People bear a disproportionate burden of medical expenses, with doctor visits and medical costs exceeding 60% for this age group [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, with the growing Older People population, it is crucial for healthcare services to tailor their offerings to address the unique needs of this demographic. This requires a comprehensive understanding of the prevalence and factors that influence arbitrary drug use in society [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChoosing a suitable framework is of utmost importance in healthcare planning [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The Protection Motivation Theory emerges as one of the most effective theories in promoting preventive behaviors. With its emphasis on motivation and skill development, this theory highlights the significance of fostering adaptive responses to threats [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, this study assesses the influential factors that contribute to the prevention of self-medication among the Older People in Ahvaz city based on the Protection Motivation Theory [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population:\u003c/h2\u003e \u003cp\u003eThis descriptive, analytical, and cross-sectional study investigated factors influencing self-medication among retired military personnel aged 60 and above, residing in Ahvaz City, Khuzestan Province, South Iran.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling Procedure and Sample Size Determination:\u003c/h3\u003e\n\u003cp\u003eA multi-stage cluster sampling approach was employed to systematically select participants. Two primary health centers located in the North Campello and Takhti areas of Ahvaz City (Health Centers No. 8 and 9) were initially chosen as clusters. Subsequently, a simple random sampling technique was applied within each center, inviting individuals aged 60 and above to participate in the study. The sample size was determined using a standard formula for estimating proportions in cross-sectional studies:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{{Z}^{2}\\times\\:p\\times\\:(1-p)}{{d}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere: (Z) represents the Z-score for the 95% confidence level (1.96), (p) is the estimated prevalence of self-medication, set at 42% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and and (d) is the margin of error, specified as 5%. Applying this formula with a prevalence estimate of 42% yielded an adequate sample size to ensure reliable data collection for assessing self-medication behaviors. During data collection, it was observed that a significant proportion of clients at these centers were retired military personnel. As a result, the study was refined to focus specifically on this demographic, and only data pertaining to military retirees were included in the final analysis.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria:\u003c/h3\u003e\n\u003cp\u003eThe study included military retirees aged 60 and above who were permanent residents of Ahvaz City and willing to participate. Individuals were excluded if they were unable to communicate effectively, had severe cognitive impairments, or declined participation.\u003c/p\u003e\n\u003ch3\u003eData Collection:\u003c/h3\u003e\n\u003cp\u003eThe data collection was conducted using a structured questionnaire comprising three distinct sections [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The first section focused on demographic information, including variables such as age, gender, education level, marital status, and health insurance status. In this section, participants also reported their self-medication practices over the past month. The second section of the questionnaire addressed constructs from the Protection Motivation Theory, which included susceptibility, severity, response cost, response efficacy, self-efficacy, rewards, behavior, and fear associated with self-medication.\u003c/p\u003e \u003cp\u003eIn addition to the standard constructs of the Protection Motivation Theory, three supplementary constructs were incorporated into the questionnaire. The first of these, \"Experience,\" assessed participants' previous engagement in self-medication, including the frequency of such behavior and the outcomes, particularly symptom improvement. The \"Barriers\" construct explored factors that contribute to self-medication, encompassing eight items such as financial constraints, lack of health insurance, overcrowded medical centers, high medical costs, and long distances to healthcare facilities. Lastly, the \"Reasons\" construct examined the motivations behind self-medication, featuring ten items that included the use of medication without a doctor's prescription for conditions such as back pain.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS version 21, with descriptive statistics such as frequencies, percentages, means, and standard deviations computed to summarize demographic variables. Pearson's correlation tests explored relationships between variables, while logistic regression analysis identified factors associated with self-medication among the elderly. A significance level of 0.05 was applied for all statistical tests. Additionally, structural equation modeling using the Maximum Likelihood method was conducted in JAMOVI v.2.4.5 to develop models elucidating the underlying mechanisms within the PMT framework [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eEthical Approval:\u003c/h2\u003e\n\u003cp\u003eThis study received ethical approval from Ahwaz University of Medical Sciences, with the code IR.AJUMS.REC.1402.494, on December 16th, 2023. The study adhered to the COPE-2009 guidelines, the Helsinki Convention 2020 rules, and its 2022 amendment to ensure confidentiality and anonymity.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eParticipant Profile:\u003c/h2\u003e\n\u003cp\u003eA total of 340 military retirees participated, with an average age of 65.07 \u0026plusmn; 7.00 years. Of the participants, 246 (72.4%) were male, while 94 (27.6%) were female. The majority were married (78.8%), and 40.9% had a diploma-level education. A notable correlation was observed between education level and self-medication prevention behaviors, particularly among those with a college education compared to those with primary, secondary, and diploma education levels (Table 1).\u003c/p\u003e\n\u003ch2\u003eProtection Motivation Theory Structures:\u003c/h2\u003e\n\u003cp\u003eTable 2 displays descriptive statistics for each PMT variable related to self-medication prevention behaviors. Variables with the highest scores included Cause (19.18 \u0026plusmn; 7.19), Response Efficiency (17.06 \u0026plusmn; 3.66), and Barriers (13.55 \u0026plusmn; 2.54), while those with the lowest scores were Resonator (4.65 \u0026plusmn; 1.02), Reward (5.29 \u0026plusmn; 2.02), and Susceptibility (5.79 \u0026plusmn; 2.06).\u003c/p\u003e\n\u003ch2\u003eCorrelation Matrix:\u003c/h2\u003e\n\u003cp\u003eThe correlation matrix (Table 3) revealed significant associations between self-medication prevention behaviors and PMT constructs, with the strongest correlation identified between Cause and self-medication prevention behaviors.\u003c/p\u003e\n\u003ch2\u003eGender Comparison Analysis:\u003c/h2\u003e\n\u003cp\u003eAn independent t-test comparing Older People men and women revealed significant differences in intensity and experience variables (P \u0026lt; 0.001), with Cohen\u0026apos;s d effect size exceeding 0.4. These results suggest that gender significantly influences the PMT model for these variables. A violin plot (Figure 1) illustrated the distribution of intensity and experience scores between genders, indicating higher scores among men.\u003c/p\u003e\n\u003ch2\u003eStructural Equation Modeling:\u003c/h2\u003e\n\u003cp\u003eFigure 2 depicts six conceptual models developed using the Maximum Likelihood method of structural equation modeling on the main PMT components. The two models with the highest fit indices (GFI \u0026amp; CFI \u0026gt; 0.9) and the lowest estimation error (RMSR \u0026amp; RMSEA \u0026lt; 0.07) based on the theoretical PMT model for interpreting drug abuse behavior in the Older People population of Khuzestan were selected. In Model 1, the causal component (CAUS) had an effect coefficient of over 60% on the behavior variable (BHV), and the becoming component (SEV) had a significant mediating effect of 43% on the fear mediator component (P \u0026lt; 0.001). In Model 2, the causal component (CAUS) again had a significant effect coefficient of over 65% on the behavior variable. The fear component (FR) had a predictive power of 34% on the reward component (RWRD), explaining 10% of the variance in the behavior variable (P \u0026lt; 0.01).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated self-medication practices among military retirees in Ahvaz City, a group characterized predominantly by married males with diverse educational backgrounds. The analysis revealed a significant correlation between education level and self-medication prevention behaviors, underscoring the crucial role of education in shaping health-related decision-making among retired military personnel. This finding aligns with previous research showing that individuals with higher education levels tend to be more aware of the risks associated with self-medication, fostering a more cautious approach to medication use\u0026nbsp;[14, 15]. Emphasizing education within this demographic could enhance understanding of the risks and consequences of self-medication, promoting safer practices among retired military individuals. This insight is particularly valuable for designing targeted educational interventions aimed at improving medication management and health outcomes among military retirees.\u003c/p\u003e\n\u003cp\u003eThe study applied the Protection Motivation Theory (PMT) framework, examining constructs such as self-efficacy, rewards, fear, response efficacy, response cost, severity, susceptibility, and behavior in relation to self-medication prevention\u0026nbsp;[10]. The findings indicated high scores in perceived severity, response efficacy, and barriers, which were essential in shaping self-medication prevention behaviors among military retirees. These results align with previous research that identifies perceived severity and response efficacy as key factors influencing self-medication decisions\u0026nbsp;[16, 17]. Additionally, gender differences were observed, with male participants reporting varying experiences and intensities in these PMT constructs. This suggests that gender-specific considerations are critical in addressing health-related behaviors among military retirees, highlighting the importance of developing gender-sensitive programs to promote responsible medication use and improve overall health outcomes in this demographic\u0026nbsp;[17].\u003c/p\u003e\n\u003cp\u003eIn addition to core PMT constructs, this study incorporated supplementary factors such as experience, barriers, and resonator effects. The \u0026quot;Experience\u0026quot; construct offered insights into previous self-medication episodes among military retirees, while \u0026quot;Barriers\u0026quot; encompassed obstacles like financial constraints and healthcare accessibility issues factors that have been consistently identified in literature as determinants of self-medication\u0026nbsp;[15]. The \u0026quot;Resonator\u0026quot; construct, though less validated, pointed to potential mediators that influence self-medication behaviors. Further research is warranted to fully understand the role of this construct in modulating self-medication within military populations.\u003c/p\u003e\n\u003cp\u003eThis study expands on existing literature by focusing specifically on military retirees and providing a more comprehensive understanding of the psychological and socio-economic determinants that shape self-medication behaviors in this unique group. The findings are consistent with previous studies on self-medication among older populations, showing that education, chronic illness, and cultural attitudes are significant predictors of self-medication\u0026nbsp;[18]. By incorporating the PMT framework, this study offers a nuanced perspective on how motivational factors affect self-medication decisions among military retirees, distinguishing this group from the general elderly population.\u003c/p\u003e\n\u003cp\u003eWhile this study\u0026rsquo;s strengths include its use of a large sample size and structural equation modeling to examine self-medication behaviors, the cross-sectional design limits causal inference. The reliance on self-reported data also introduces potential recall bias, and the focus on military retirees in Ahvaz City may limit generalizability to other populations [19, 20]. Future research could benefit from longitudinal studies to assess changes in self-medication behaviors over time, as well as qualitative approaches to explore cultural and contextual influences. Expanding this research to other regions and including diverse military retiree populations could enhance understanding of self-medication behaviors across different contexts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides valuable insights into self-medication practices among military retirees in Ahvaz City, emphasizing the importance of education as a key determinant in preventing self-medication behaviors. The PMT framework, complemented by additional constructs, identified significant motivational factors such as perceived severity, response efficacy, and barriers that influence self-medication practices in this demographic. Observed gender differences suggest a need for tailored, gender-sensitive interventions to promote safe medication use. These findings contribute to the growing body of research on self-medication among military retirees and highlight the need for targeted strategies to improve healthcare practices for this specific group.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003ethics approval and consent to participate\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from Ahvaz Jundishapur University of Medical Sciences (Ethics code: IR.AJUMS.REC.1402.494, approved on December 16, 2023). Informed consent was obtained from all participants, ensuring their voluntary involvement and confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided consent for publication of the anonymized data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eompeting interests\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings of this study are available from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their gratitude to Ahvaz Jundishapur University of Medical Sciences for providing ethical approval and to the participants for their invaluable contributions to this research.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthors\u0026apos; information \u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDetails on authors\u0026rsquo; affiliations and contributions are included in the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthors\u0026apos; contributions \u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNasser Hatamzadeh\u003c/strong\u003e: Conceptualized the study design, led data acquisition, performed data analysis, and contributed to drafting and critically reviewing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbdolrahim Asadollahi\u003c/strong\u003e: Assisted with data collection, conducted statistical analyses, and contributed to the interpretation of results and manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFatemeh Adlirad\u003c/strong\u003e: Conducted the literature review, supported data collection, contributed to data organization, and assisted in preliminary data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSamaneh Motalebi\u003c/strong\u003e: Played a role in data management, contributed to data analysis, and reviewed the manuscript for intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNajmeh Yazdanparast\u003c/strong\u003e: Participated in data collection, assisted in data interpretation, and provided critical revisions of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFatemeh Modhej\u003c/strong\u003e: Participated in data collection, assisted in data interpretation, and provided critical revisions of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMorteza Abdullatif Khafaie\u003c/strong\u003e: Served as the corresponding author, supervised the entire research project, ensured the integrity and accuracy of the work, and provided final approval of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cspan dir=\"RTL\"\u003e\u003c/span\u003ePapakosta M, Zavras D, Niakas D: \u003cstrong\u003eInvestigating factors of self-care orientation and self-medication use in a Greek rural area\u003c/strong\u003e. \u003cem\u003eRural and remote health \u003c/em\u003e2014, \u003cstrong\u003e14\u003c/strong\u003e:2349.\u003c/li\u003e\n\u003cli\u003e\u003cspan dir=\"RTL\"\u003e\u003c/span\u003eR. Tajik MS, M. Shamsi MS, A. 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2234-2241\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003eLiu S, Li, Y., \u0026amp; Wang, Z: \u003cstrong\u003eSelf-medication behaviors among elderly populations: A cross-sectional analysis\u003c/strong\u003e. \u003cem\u003eBMC Public Health \u003c/em\u003e2006, \u003cstrong\u003e6, 152.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003eTaylor D, Turner, M., \u0026amp; Karimi, A: \u003cstrong\u003eSelf-medication and healthcare avoidance behaviors: Cross-sectional analysis of military retirees\u003c/strong\u003e. \u003cem\u003eMilitary Medicine \u003c/em\u003e2019, \u003cstrong\u003e184(1), 46-53\u003c/strong\u003e.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Descriptive Statistics and Correlations of Demographic Variables and Self-Medication Prevention Behaviors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0607%;\"\u003e\n \u003cp\u003eMean (SD)/ Frequencies\u003cspan dir=\"RTL\"\u003e\u0026nbsp;(%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57.0762%;\"\u003e\n \u003cp\u003eBehaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.2131%;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0607%;\"\u003e\n \u003cp\u003e67.05 (7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003er = 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.2131%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e264 (72.4%)\u003c/p\u003e\n \u003cp\u003e94 (27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003et = -0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.2131%;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e72 (22.2%)\u003c/p\u003e\n \u003cp\u003e268 (78.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003et = 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.2131%;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003cp\u003eColleges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49 (14.4%)\u003c/p\u003e\n \u003cp\u003e70 (20.6%)\u003c/p\u003e\n \u003cp\u003e139 (40.9%)\u003c/p\u003e\n \u003cp\u003e82 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8631%;\"\u003e\n \u003cp\u003eF = 6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.2131%;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e* Using anova, P \u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: The Structures of the Protection Motivation Theory in Self-Medication Prevention Behaviors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMinimum-Maximum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eSelf-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e9.29)\u003cspan dir=\"RTL\"\u003e)\u003c/span\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e3-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eReward\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e5.29(2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e2-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eFear\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e11.90(3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e4-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eBehavior\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e7.80(3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e4-20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eResponse efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e17.06(3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e5-20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eResponse cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e7.85(2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e3-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eSeverity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e6.72(1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e2-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eSusceptibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e5.79(2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e2-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003cem\u003eOther factors affecting self-medication that have been added to the protection motivation theory:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eexperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e4.65(1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e3-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eBarriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e13.55(2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e8-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eResonators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e19.18(7.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e10-50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Pearson\u0026apos;s Correlation Coefficient Matrix of Protection Motivation Theory Constructs in Understanding Drug Use among military retirees Residents in Ahvaz City\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable dir=\"rtl\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003eBehavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003eBarriers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003eResonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003eexperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003eSusceptibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003eSeverity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003eResponse cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003eResponse effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003eFear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003eReward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003eSelf-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eSelf-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003eR= -0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eReward\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.33**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eFear\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.33**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eResponse effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.305**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.45**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eResponse cost\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.45**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.27**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.511**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eSeverity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.41**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.36**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.117*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.48**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eSusceptibility\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.22**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.22**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eexperience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.274**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.131*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eResonator\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.536\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.248**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.187**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.23**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.28**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.27**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eBarriers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4883%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.58034%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.150**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.236**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.692**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.140**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.67239%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.21**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11232%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.33229%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=0.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.64431%;\"\u003e\n \u003cp dir=\"LTR\"\u003er=-0.22**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2964%;\"\u003e\n \u003cp dir=\"LTR\"\u003eBehavior\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;(Note: ** indicates statistical significance, P \u0026lt; 0.05)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Gender Comparison through Independent Samples T-Test in Understanding Drug Use among military retirees\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEffect Size: Cohen\u0026rsquo;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Susceptibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceived Severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResponse Cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResponse Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSelf-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBehavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003cem\u003eOther factors affecting self-medication that have been added to the protection motivation theory:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBarrier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"self-medication, elderly, Protection Motivation Theory, education, gender, Iran, healthcare interventions","lastPublishedDoi":"10.21203/rs.3.rs-5360936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5360936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eSelf-medication is prevalent among the elderly population, posing significant health risks. In Iran, older adults frequently engage in self-medication due to various socio-economic, cultural, and health-related factors. This study aimed to examine the determinants of self-medication among older adults in Ahvaz, Iran, using the Protection Motivation Theory (PMT) framework.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted with 340 military retirees aged 60 and above in Ahvaz City. Data were collected using a structured questionnaire that gathered demographic information and assessed constructs from the Protection Motivation Theory (PMT), including self-efficacy, susceptibility, severity, response cost, and behavior. Additional factors, such as experience, barriers, and resonators, were also evaluated. Data analysis was performed using SPSS, with structural equation modeling conducted in JAMOVI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean age of participants was 67.05 years (SD\u0026thinsp;=\u0026thinsp;7.00), with 72.4% males and 78.8% married individuals. Education was significantly correlated with self-medication prevention behaviors (ANOVA, p\u0026thinsp;=\u0026thinsp;0.002), where higher education levels promoted more responsible medication management. Among PMT variables, response efficacy (mean\u0026thinsp;=\u0026thinsp;17.06, SD\u0026thinsp;=\u0026thinsp;3.66) and severity (mean\u0026thinsp;=\u0026thinsp;6.72, SD\u0026thinsp;=\u0026thinsp;1.90) showed the highest influence on behavior. The study also revealed a significant positive correlation between behavior and barriers (r\u0026thinsp;=\u0026thinsp;0.536, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), resonator (r\u0026thinsp;=\u0026thinsp;0.692, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and response effectiveness (r\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Gender differences were observed, with men scoring higher in perceived severity (p\u0026thinsp;=\u0026thinsp;0.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.432) and resonators (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.442). Furthermore, experience (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.443) was a significant factor affecting self-medication prevention behaviors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEducation and gender play a crucial role in shaping self-medication prevention behaviors among older adults. Higher education levels corresponded to better medication management, while gender-specific differences were evident in key PMT constructs. These findings provide critical insights for developing targeted interventions to promote safe self-medication practices and improve healthcare outcomes for older adults.\u003c/p\u003e","manuscriptTitle":"Determinants of Self-Medication among Military Retirees in South-west Iran: A Protection Motivation Theory Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-04 00:07:25","doi":"10.21203/rs.3.rs-5360936/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eafbcceb-61e0-4594-ad66-965a6d729b2b","owner":[],"postedDate":"December 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T06:24:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-04 00:07:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5360936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5360936","identity":"rs-5360936","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}