Antecedents of Sexual and Reproductive Health Misperceptions–Model

Abstract

The Antecedents of Sexual and Reproductive Health Misperceptions–Model (ASRHM-M) is an empirically validated psychometric and structural assessment framework developed by Dong, Zhang, Lam, and Huang (2024). Designed to investigate the cognitive, emotional, and social determinants driving false beliefs in sexual and reproductive health (SRH), the instrument operationalizes a multidimensional system capturing stigma perceptions, active information avoidance, cognitive information overload, misinformation exposure frequency, and explicit endorsement of SRH misperceptions. Administered electronically to adult female populations, the core battery comprises 22 total items across its structural dimensions, with 13 focal evaluative items measuring the endorsement of concrete SRH misinformation statements spanning contraception, sexually transmitted infections (STIs/STDs), fertility myths, oncology misconceptions, and sociocultural health fallacies. Responses across antecedent constructs are assessed via 7-point Likert scales (ranging from 1 = strongly disagree to 7 = strongly agree for stigma, avoidance, and overload; 1 = never to 7 = very often for exposure frequency), whereas the misperception endorsement items are calibrated on a 7-point truth-judgment continuum from 1 (very false) to 7 (very true). Psychometric evaluation using structural equation modeling (SEM) and confirmatory factor analysis (CFA) demonstrated strong construct validity and goodness-of-fit (χ²/df = 2.728, CFI = .941, RMSEA = .050, SRMR = .043). The subscales exhibit robust internal consistency, with Cronbach’s alpha coefficients ranging from .78 to .90 and composite reliability (CR) values exceeding .80. The model provides an essential diagnostic and evaluative tool for public health researchers, epidemiologists, gynecological clinicians, and behavioral scientists seeking to map health literacy deficits, track infodemic vulnerabilities, and implement targeted educational interventions.

Keywords

Sexual and Reproductive Health, Misperceptions, Health Misinformation, Stigma Perceptions, Information Overload, Information Avoidance, Psychometrics, Structural Equation Modeling, Contraceptive Myths, Women’s Health

Authors

The Antecedents of Sexual and Reproductive Health Misperceptions–Model was conceptualized, operationalized, and psychometrically validated by an interdisciplinary research team specializing in health communication, public health, and reproductive medicine:

• Yujie Dong — School of Media and Communication, Shanghai Jiao Tong University, Shanghai, China. (ORCID: Yujie Dong).
• Lianshan Zhang (Corresponding Author) — Associate Professor, School of Media and Communication, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China. Email: [email protected].
• Chervin Lam — Department of Communication, University of California, Davis, Davis, California, United States.
• Zhongwei Huang — Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; and National University Health System, Singapore.

Purpose

The primary purpose of the Antecedents of Sexual and Reproductive Health Misperceptions–Model is to systematically delineate, quantify, and explain the etiology of false beliefs regarding sexual and reproductive functioning, contraception, disease transmission, and gynecological care. In contemporary global healthcare environments—characterized by rapid digital communication, algorithmic echo chambers, and persistent sociocultural taboos—individuals are increasingly vulnerable to pervasive medical misinformation. Despite widespread availability of evidence-based guidelines, deep-seated fallacies persist among general populations, frequently resulting in delayed healthcare seeking, non-compliance with contraceptive regimens, elevated rates of unintended pregnancies, transmission of preventable sexually transmitted infections, and severe emotional distress.

From an applied clinical and public health perspective, the instrument was engineered to move beyond simple knowledge-testing checklists. While conventional instruments measure factual knowledge as a binary correct/incorrect variable, the ASRHM-M captures the strength of belief in prevalent misconceptions along an epistemic spectrum, alongside the psycho-social and environmental antecedents that nurture these cognitive errors. By capturing constructs such as perceived social stigma, cognitive exhaustion from information volume (information overload), and self-protective avoidance behaviors, the scale reveals why individuals internalize flawed medical narratives even when accurate healthcare resources exist.

The scale serves multiple complementary functions across academic and clinical settings:

• Epidemiological Surveillance and Health Communication: It enables public health researchers to identify demographic clusters most susceptible to unverified health claims, tracking how algorithmic information environments spread medical pseudo-science.
• Intervention Design and Evaluation: Clinicians and health educators can utilize the instrument as a baseline and post-intervention metric to evaluate the efficacy of counter-messaging campaigns, debunking strategies, and reproductive health literacy workshops.
• Diagnostic Assessment in Gynecological Care: Routine clinical intake protocols in women’s health clinics can deploy the instrument to identify individual cognitive barriers that impede patient adherence to clinical advice, such as unwarranted fears surrounding hormonal therapies, intrauterine devices, or preconception screenings.
• Cross-Cultural Theoretical Testing: By evaluating how sociocultural stigmas exacerbate cognitive processing deficits, the model provides an empirical bridge between communication studies, cognitive psychology, and reproductive medicine.

Psychological Construct

The theoretical architecture of the ASRHM-M comprises five interconnected latent constructs organized into an antecedent-to-outcome structural chain. These constructs capture the social, emotional, cognitive, and epistemic dimensions of health decision-making:

1. Stigma Perceptions

This construct captures an individual’s subjective appraisal of the social sanctions, moral devaluations, and interpersonal discrimination directed toward women experiencing sexual and reproductive disorders. Rooted in Goffman’s sociological formulation of social stigma, it evaluates the perception of SRH disorders as socially contaminating attributes. Example items assess the belief that afflicted women are regarded as “unclean,” that communities will “think badly” of them, or that peers will actively “avoid” them. Elevated perceived stigma generates intense psychological vulnerability, shame, and perceived reputational threat, driving individuals to withdraw from normative institutional healthcare channels.

2. Information Avoidance

Operationalized as a defensive coping mechanism, information avoidance reflects the deliberate, intentional effort to evade threatening, distressing, or anxiety-provoking health facts. When sexual and reproductive health topics are heavily stigmatized, obtaining factual information risks triggering cognitive dissonance, moral anxiety, or social exposure. Measured items assess inclinations such as deliberately avoiding reading health materials, preferring not to contemplate one’s own reproductive health, and actively rejecting supplementary healthcare communications.

3. Information Overload

Drawing from cognitive load theory and modern infodemiology, this construct quantifies subjective cognitive exhaustion and perceived incapacity to process excessive, conflicting, or overly complex health communications. Within modern online spaces, contradictory health claims regarding diet, contraception, fertility, and wellness overwhelm the individual’s bounded processing capacity. Items capture the sensation of being paralyzed by contradictory recommendations (“there are so many recommendations… it’s hard to know which ones to follow”), feeling overwhelmed by mandatory health literacy, and experiencing cognitive burnout leading to apathy (“I don’t even care to hear new information”).

4. Misinformation Exposure

Misinformation exposure reflects the self-reported frequency with which an individual encounters scientifically inaccurate, fabricated, or unverified assertions regarding sexual and reproductive health across offline social networks, digital platforms, and popular media. Measured on a behavioral frequency scale, this dimension reflects environmental immersion within low-credibility information ecologies, serving as the empirical baseline for the illusory truth effect.

5. Sexual and Reproductive Health Misperceptions

The primary outcome construct represents the explicit epistemic endorsement of factually incorrect health propositions. Rather than general ignorance, misperceptions represent active confidence in false premises. The scale operationalizes 13 verified items spanning five major sub-domains:

• Contraceptive Fallacies: E.g., the belief that coitus interruptus (withdrawal) or post-coital urination/showering prevents pregnancy; that double-condom use enhances barrier efficacy; or that emergency contraception exhibits 100% post-coital efficacy.
• Sexually Transmitted Infection (STI) Transmission Fallacies: E.g., the conviction that oral contraceptive pills confer prophylaxis against STIs; that STIs require penetrative intercourse; or that asymptomatic carriers cannot transmit pathogens.
• Fertility and Conception Fallacies: E.g., the absolute belief that unprotected intercourse during the “safe period” carries zero risk of pregnancy, or that frequent coitus during ovulation guarantees conception.
• Sociocultural and Medical Fallacies: E.g., the assertion that severe dysmenorrhea naturally ceases following parturition, or that preconception screenings are solely mandatory for females.
• Alternative Medicine and Oncological Myths: E.g., claims that traditional Chinese medicine formulations can cure HIV/AIDS, or that regular soy milk consumption induces breast or ovarian oncogenesis.

Theoretical Framework

The conceptual underpinning of the ASRHM-M integrates principles from the Health Belief Model, the Stigma Management Communication theory, the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP), and contemporary epistemic cognitive psychology.

At its core, the model posits that misperceptions do not emerge in an informational vacuum; rather, they are the joint product of social inhibitions, bounded cognitive architectures, and polluted information ecologies. Historically, classical rational-actor models presumed that individuals exposed to health facts would logically assimilate them to maximize personal utility. However, the ASRHM-M models the structural vulnerabilities that disrupt rational processing:

First, the framework incorporates stigma theories (Goffman, 1963; Link & Phelan, 2001). In societies where sexual conduct and reproductive pathology remain morally policed, health conditions are linked to social devaluation. To protect self-identity and prevent anticipated discrimination, individuals mobilize avoidance mechanisms. By shutting down active information seeking and evading formal clinical dialogues, individuals forfeit opportunities to correct erroneous beliefs through authoritative channels.

Second, the model builds upon cognitive load theory (Sweller, 1988) and Annie Lang’s LC4MP framework (2000). The human cognitive system possesses finite working memory resources for encoding, storage, and retrieval. When health messaging is saturated with ambiguous, complex, or sensationalized digital claims, individuals experience cognitive overload. Overload depletes the cognitive resources required for systematic, analytical scrutiny (dual-process theory’s System 2). Under conditions of mental depletion, individuals resort to heuristic, low-effort processing (System 1).

Third, the framework operationalizes the illusory truth effect (Hasher, Goldstein, & Toppino, 1977; Pennycook & Rand, 2021). When cognitive capacity is constrained by information overload, repeated exposure to misinformation across social circles and digital platforms increases processing fluency. Humans routinely substitute subjective processing ease for epistemic truth; statements encountered repeatedly are perceived as intuitively true, even when scientifically baseless. Consequently, perceived stigma and information overload operate as indirect engines that stimulate avoidance and heuristic reliance, allowing frequent misinformation exposure to solidify into deeply held medical misperceptions.

Validity

The psychometric validity of the ASRHM-M was rigorously assessed by Dong et al. (2024) through a multifaceted validation protocol administered to an empirical sample of adult Chinese women, adhering to standards established by the American Educational Research Association (AERA), the American Psychological Association (APA), and the National Council on Measurement in Education (NCME).

Construct and Structural Validity

Construct validity was evaluated using full-information structural equation modeling and confirmatory factor analysis. The hypothesized five-factor latent structure demonstrated exceptional fit to the empirical data across multiple standard global fit criteria:

• Relative Chi-Square (χ²/df): 2.728, falling well within the conventional conservative threshold of < 3.0, indicating acceptable discrepancy between the sample covariance matrix and the implied model matrix.
• Comparative Fit Index (CFI): .941, surpassing the standard benchmark for adequate model fit (> .90) and approaching optimal thresholds (> .95).
• Root Mean Square Error of Approximation (RMSEA): .050 (90% CI [.044, .056]), demonstrating close population approximation and low approximation error per degree of freedom.
• Standardized Root Mean Square Residual (SRMR): .043, substantially below the conservative cut-off limit of < .08, verifying minimal average standardized residual variance.

Convergent and Discriminant Validity

Convergent validity evaluates the extent to which indicators of a specific latent construct share a high proportion of common variance. During the CFA refinement process, items demonstrating standardized factor loadings below the critical threshold of 0.50 were eliminated (specifically, Item 4, Item 13, and Item 14 of the preliminary comprehensive pool were dropped). In the final refined model, the vast majority of standardized factor loadings exceeded 0.60 (ranging predominantly from .62 to .88, p < .001). Composite reliability (CR) metrics across all latent dimensions exceeded the .80 benchmark, demonstrating that the observed items consistently converge upon their theoretical constructs.

Discriminant validity was established through Fornell–Larcker criteria and examination of cross-loadings. The average variance extracted (AVE) for each latent construct exceeded the squared correlation coefficients between that construct and all other latent variables in the model. Furthermore, structural path analyses confirmed distinct directional vectors between perceived stigma, information overload, avoidance behaviors, and the explicit endorsement of false statements, validating that the constructs reflect functionally and empirically independent psychological phenomena rather than overlapping measurement artifacts.

Reliability

The internal consistency and precision of the ASRHM-M subscales were verified through extensive psychometric testing. Reliability was evaluated using both classical test theory indices (Cronbach’s alpha) and structural equation modeling parameters (composite reliability).

Across the validated latent dimensions, the instrument demonstrated high internal consistency:

• Cronbach’s Alpha (α): Subscale alpha coefficients ranged consistently between .78 and .90, demonstrating strong homogeneity of items within each sub-dimension without exhibiting excessive redundancy (α > .95). Specifically, the Stigma Perceptions subscale yielded α = .86; Information Avoidance achieved α = .84; Information Overload achieved α = .88; Misinformation Exposure achieved α = .82; and the SRH Misperceptions composite scale yielded α = .78 to .85 depending on sub-sample stratification.
• Composite Reliability (CR): To correct for the known limitations of Cronbach’s alpha (such as its assumption of tau-equivalence), composite reliability was computed for all measurement models. All CR values surpassed .80, confirming excellent construct-level variance capture relative to random measurement error.
• Standard Error of Measurement (SEM): Item-level standard error indices remained low across the 7-point continuum, confirming that the scale maintains high discriminative sensitivity at both moderate and extreme levels of misperception endorsement.

Factor Analysis

The latent factor structure of the ASRHM-M was tested using structural equation modeling in AMOS and Mplus. The psychometric workflow combined exploratory factor screening during preliminary pilot phases with confirmatory factor analysis (CFA) in the definitive validation study.

Confirmatory Factor Structure

The final validated measurement model consists of five distinct, correlated latent factors representing:

1. F1: Stigma Perceptions (measured by items evaluating social sanctions, perceived dirtiness, and anticipated interpersonal ostracism).
2. F2: Information Avoidance (measured by items assessing defensive behavioral rejection of SRH topics and deliberate media evasion).
3. F3: Information Overload (measured by items assessing cognitive fatigue, inability to discern valid guidelines, and information burnout).
4. F4: Misinformation Exposure (measured by frequency of encountering unverified health claims across digital and interpersonal channels).
5. F5: SRH Misperceptions (measured by the explicit endorsement of false medical statements regarding contraception, STIs, cancer, and reproduction).

Item Retention and Deletion Dynamics

Rigorous psychometric purification criteria were applied during CFA execution. Items exhibiting standardized factor loadings below 0.50 or substantial cross-loadings across non-hypothesized factors were systematically excluded to preserve unidimensionality within subscales:

• Item Exclusions: Preliminary items 4, 13, and 14 were dropped due to standardized factor loadings below the .50 threshold, which indicated that their variance was dominated by unique error rather than the underlying latent construct.
• Retained Items: All retained items exhibited statistically significant factor loadings (p < .001) ranging from .61 to .89. For the Misperception outcome battery, the 13 focal items exhibited robust loadings onto the overarching misperception domain, confirming that endorsement of seemingly disparate myths (e.g., traditional medicine curing AIDS vs. soy milk causing breast cancer) is driven by a unified underlying propensity toward medical misinformation acceptance.

Instrument / Measurement Tool

The complete ASRHM-M battery is structured as an electronic, self-administered survey inventory designed for clinical research, epidemiological surveys, and health communication assessments. Below is the operational summary of the instrument’s technical specifications:

• Instrument Name: Antecedents of Sexual and Reproductive Health Misperceptions–Model (ASRHM-M)
• Authors: Yujie Dong, Lianshan Zhang, Chervin Lam, and Zhongwei Huang (2024)
• Administration Format: Computer-assisted web interviewing (CAWI), mobile digital survey, or electronic clinical intake
• Target Population: Adult female populations (validated in adult women aged 18 and older)
• Total Structural Items: 22 items across the comprehensive SEM structural model
• Misperception Battery Items: 13 focal evaluative items measuring the endorsement of concrete SRH misinformation statements
• Response Formats:
  • Stigma Perceptions: 7-point Likert scale (1 = Strongly Disagree, 2 = Disagree, 3 = Somewhat Disagree, 4 = Neither Agree nor Disagree, 5 = Somewhat Agree, 6 = Agree, 7 = Strongly Agree)
  • Information Avoidance: 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree)
  • Information Overload: 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree)
  • Misinformation Exposure: 7-point Frequency scale (1 = Never, 2 = Very Rarely, 3 = Rarely, 4 = Occasionally, 5 = Frequently, 6 = Very Frequently, 7 = Very Often)
  • SRH Misperceptions (Misinformation Statements): 7-point Epistemic Truth scale (1 = Very False, 2 = False, 3 = Somewhat False, 4 = Uncertain / Neutral, 5 = Somewhat True, 6 = True, 7 = Very True)
• Scoring Protocol:
  • All 13 misinformation statements are factually false according to established international medical consensus (WHO, CDC, ACOG).
  • Higher numerical scores (5–7) indicate severe misperception endorsement, whereas lower scores (1–3) indicate accurate factual rejection of misinformation.
  • Subscale scores are derived by calculating the unweighted arithmetic mean of the respective items, yielding a composite score from 1.0 to 7.0 for each latent dimension.
  • Global structural scores can be analyzed using latent variable modeling in SEM software (e.g., Mplus, AMOS, R package lavaan) utilizing the covariance matrix parameters established by Dong et al. (2024).

Permissions & Fee and Test Year

The Antecedents of Sexual and Reproductive Health Misperceptions–Model was formally published in 2024. The instrument is non-commercial, and no fee is required for its administration in non-profit academic research, epidemiological investigations, or public health clinical evaluations.

The scale was developed and disseminated under standard academic fair-use and copyright arrangements via Elsevier in Patient Education and Counseling. Researchers and clinicians wishing to employ the complete structural battery or adapt its items for regional health literacy interventions are encouraged to cite the original empirical publication (Dong et al., 2024). For commercial adaptations, institutional translations, or formal copyright permissions beyond academic research fair use, inquiries should be directed to the corresponding author, Dr. Lianshan Zhang, at Shanghai Jiao Tong University ([email protected]), or through the RightsLink permissions gateway of the publisher.

References

Below are primary academic works and theoretical foundations documenting the development, psychometric calibration, and operationalization of the ASRHM-M:

• Chae, J. (2015). Who avoids cancer information? Examining a psychological process from cancer fatalism to cancer information avoidance through cancer worry. Communication Research, 43(8), 1043–1066. https://doi.org/10.1177/0093650215579222
• Cunningham, S. D. (2002). Correlates of perceived stigma among adolescents with sexually transmitted diseases. Journal of Adolescent Health, 30(2), 112. https://doi.org/10.1016/S1054-139X(01)00388-3
• Dong, Y., Zhang, L., Lam, C., & Huang, Z. (2024). Counteracting sexual and reproductive health misperceptions: Investigating the roles of stigma, misinformation exposure, and information overload. Patient Education and Counseling, 120, Article 108098. https://doi.org/10.1016/j.pec.2023.108098
• Goffman, E. (1963). Stigma: Notes on the management of spoiled identity. Prentice-Hall.
• Hasher, L., Goldstein, D., & Toppino, T. (1977). Frequency and the conference of referential validity. Journal of Verbal Learning and Verbal Behavior, 16(1), 107–112. https://doi.org/10.1016/S0022-5371(77)80012-1
• Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70. https://doi.org/10.1111/j.1460-2466.2000.tb02833.x
• Link, B. G., & Phelan, J. C. (2001). Conceptualizing stigma. Annual Review of Sociology, 27(1), 363–385. https://doi.org/10.1146/annurev.soc.27.1.363
• Pennycook, G., & Rand, D. G. (2021). The psychology of fake news. Trends in Cognitive Sciences, 25(5), 388–402. https://doi.org/10.1016/j.tics.2021.02.007
• Ramondt, S., & Ramírez, A. S. (2019). Information overload, processing, and avoidance: A study of cancer information among Latino immigrants. Journal of Health Communication, 24(7), 543–552. https://doi.org/10.1080/10810730.2019.1633519
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Items of the Scale