Healthcare Seeking Likelihood

Abstract

The Healthcare Seeking Likelihood scale is a concise, highly sensitive psychometric instrument designed to evaluate an individual's behavioral intention to pursue clinical consultation, diagnostic testing, and preventative information regarding medical conditions. Originally operationalized and validated by Chethana Achar, Nidhi Agrawal, and Meng-Hua Hsieh (2020) in the Journal of Marketing Research, the instrument emerged from empirical research investigating the interplay between fear appeals, psychological distance, and preventative action. The scale comprises three target items rated on a 7-point Likert-type response scale ranging from 1 (“Not at all likely”) to 7 (“Very likely”). Psychometrically, the measure demonstrates robust unifactorial dimensionality, excellent internal consistency (with Cronbach's alpha coefficients consistently exceeding .85 across experimental replications), and strong convergent, discriminant, and predictive validity. By capturing critical behavioral inclinations—such as scheduling medical consultations, seeking diagnostic clarity, and obtaining preventive guidance—the Healthcare Seeking Likelihood index serves as an indispensable tool in health communication, behavioral economics, preventive oncology, and health psychology.

Keywords

Healthcare seeking likelihood, health behavior, fear appeals, preventive health, screening intentions, construal level theory, health communication, behavioral intention, medical compliance, psychometrics

Authors

The scale was developed and validated by a team of scholars specializing in consumer behavior, health communication, and marketing psychology:

• Chethana Achar, Ph.D. — Assistant Professor of Marketing, A. B. Freeman School of Business, Tulane University (formerly at Michael G. Foster School of Business, University of Washington). Her research centers on consumer health decision-making, social marketing, and emotional drivers of preventive health behaviors.
• Nidhi Agrawal, Ph.D. — Michael G. Foster Endowed Professor of Marketing, Michael G. Foster School of Business, University of Washington. Professor Agrawal is an authority on health information processing, self-regulation, emotion, and consumer well-being.
• Meng-Hua Hsieh, Ph.D. — Associate Professor of Marketing, College of Business and Public Policy, Pennsylvania State University Harrisburg (formerly at California State University, Los Angeles). Her scholarship examines consumer information processing, message framing, and healthcare interventions.

Purpose

The primary purpose of the Healthcare Seeking Likelihood instrument is to quantify individuals' prospective intentions to engage with professional healthcare infrastructures following exposure to health risk information. Despite advances in medical diagnostics and therapeutic interventions, patient delay in seeking clinical evaluation remains a leading contributor to preventable morbidity and mortality. Health-related avoidance is especially pronounced when individuals encounter threatening diagnostic possibilities, such as cancer, human immunodeficiency virus (HIV), or chronic cardiovascular disease. This scale was explicitly engineered to address the paradoxical phenomenon whereby heightening awareness of health threats often induces psychological threat avoidance rather than adaptive healthcare utilization.

In clinical trials and epidemiological research, measuring overt, distal clinical outcomes (such as documented clinic visits, laboratory draws, or biopsy completion) can be cost-prohibitive, ethically complex, and vulnerable to external structural barriers (e.g., insurance coverage, transportation, clinician availability). The Healthcare Seeking Likelihood instrument functions as a validated proxy for proximal decision-making, isolating motivational readiness from logistical impediments. It allows clinical researchers to assess how various communicative stimuli, psychological mindsets, or psychoeducational programs modulate an individual's willingness to cross the threshold from passive awareness to active engagement with the healthcare delivery system.

Furthermore, in applied public health and health communication settings, the scale offers an agile, low-burden evaluation mechanism for pretesting message appeals. Campaign designers routinely employ this metric to determine whether fear-based, gain-framed, loss-framed, or construal-matched messages effectively stimulate adaptive health-seeking behaviors rather than defensive coping responses. Its theoretical sensitivity makes it an ideal dependent measure across varied medical domains, including routine screening mammography, colorectal screening, genetic testing, and lifestyle-related metabolic disorder assessments.

Psychological Construct

The construct captured by this instrument is Healthcare Seeking Likelihood, conceptualized as a focused, deliberate behavioral intention toward professional medical engagement and health-related information acquisition. Rooted in the social-cognitive paradigm of behavioral prediction, behavioral intentions reflect the immediate cognitive antecedents of volitional behavior, encapsulating how much effort an individual plans to exert to execute a specific course of action (Ajzen, 1991).

Healthcare seeking likelihood encompasses three distinct yet deeply intertwined behavioral facets:

• Proactive Professional Consultation: The intention to initiate contact and coordinate a formal diagnostic consultation with a physician or credentialed healthcare professional (captured by Item 1). This reflects an active commitment of time, logistical effort, and social interaction to confront a potential health vulnerability.
• Epistemic Information Acquisition: The proactive motivation to discover, process, and absorb preventative and diagnostic knowledge regarding the target pathology (captured by Item 2). Unlike passive message reception, this behavioral facet represents purposeful search efforts to eliminate epistemic ambiguity regarding the disease etiology and prevention regimens.
• Diagnostic Screening Engagement: The direct willingness to broach, discuss, and undergo formal diagnostic or screening procedures with a medical provider (captured by Item 3). This represents the most psychologically threatening dimension of healthcare seeking, as diagnostic testing carries the imminent prospect of confirming the presence of pathology.

Critically, the construct isolates purposeful healthcare navigation from passive risk appraisal. While an individual may acknowledge high personal vulnerability and disease severity, such cognitions do not automatically translate into healthcare seeking. Rather, healthcare seeking likelihood captures the operationalized translation of risk appraisal and efficacy assessments into actionable, proactive behavioral pathways.

Theoretical Framework

The theoretical foundations of the Healthcare Seeking Likelihood scale are anchored at the intersection of Construal Level Theory (CLT; Trope & Liberman, 2010) and contemporary models of health risk communication, including the Extended Parallel Process Model (EPPM; Witte, 1992) and Protection Motivation Theory (PMT; Rogers, 1983).

According to Construal Level Theory, psychological distance (whether temporal, spatial, social, or hypothetical) systematically alters the way individuals cognitively represent events. Distant events are processed via high-level, abstract, and goal-relevant construals (“why” an action is taken), whereas psychologically proximal events are evaluated through low-level, concrete, and process-oriented construals (“how” an action is executed). Achar, Agrawal, and Hsieh (2020) demonstrated that health communications typically evoke two concurrent, competing psychological drivers:

1. Fear of Detection: A concrete, visceral, and psychologically immediate affective reaction centered on the prospect of learning that one has a debilitating or terminal illness. Because fear of detection involves immediate, emotionally vivid consequences, it is naturally activated by low-level, concrete construals.
2. Efficacy of Prevention: An abstract, prospective, and goal-directed cognitive belief that taking preventive measures can avert future morbidity and maintain long-term well-being. Because prevention efficacy represents long-term strategic values, it aligns naturally with high-level, abstract construals.

When public health interventions inadvertently trigger concrete processing alongside high perceived health threat, individuals experience an acute surge in the fear of detection, precipitating defensive avoidance, denial, and diminished healthcare seeking. Conversely, when messaging aligns high-level abstract construals with prevention efficacy, the perceived value of preventive actions outweighs the acute dread of diagnostic detection. The Healthcare Seeking Likelihood scale was derived specifically to capture the downstream outcome of this cognitive-affective calculus, providing a direct metric of whether an intervention successfully resolves the tension between detection-related dread and prevention-oriented motivation.

Validity

The psychometric validity of the Healthcare Seeking Likelihood instrument has been rigorously established across multiple controlled experimental investigations and diverse clinical scenarios, including sexually transmitted infections (STIs), oncological screening (e.g., skin and colorectal cancer), and metabolic diseases.

Construct and Content Validity

Content validity was ensured during scale construction by generating items that comprehensively span the functional continuum of outpatient healthcare engagement: initiating provider contact, seeking disease-specific education, and explicitly discussing diagnostic evaluation. Psychometric reviews verified that the wording is transparent, free from medical jargon, and universally applicable across diverse demographic cohorts.

Convergent and Discriminant Validity

Convergent validity is documented through substantial, statistically significant positive correlations with related health-protective constructs. Across studies in Achar et al. (2020), scores on the Healthcare Seeking Likelihood scale correlated positively with perceived message persuasiveness ($r \approx .54$ to $.68, p < .001$), prevention efficacy beliefs ($r \approx .45$ to $.61, p < .001$), and subjective attitudes toward medical testing. Discriminant validity was demonstrated through low to non-significant correlations with unrelated consumer constructs, general mood indices, and dispositional risk aversion ($r < .15$), demonstrating that the instrument assesses specific health engagement intentions rather than generalized positive affect or demand characteristics.

Predictive and Criterion Validity

The instrument displays exceptional predictive validity. In laboratory and online experimental paradigms, higher scores on the 3-item Healthcare Seeking Likelihood composite reliably predicted consequential, real-time behavioral commitments. Specifically, respondents reporting elevated healthcare seeking likelihood exhibited significantly higher rates of clicking external diagnostic clinic locators, ordering home screening test kits, and downloading preventative medical guidelines ($eta$ coefficients ranging from $.38$ to $.52, p < .001$). The scale thus bridges the gap between hypothetical self-report and concrete behavioral follow-through.

Reliability

The reliability of the Healthcare Seeking Likelihood scale has been substantiated through rigorous classical test theory paradigms. Despite its brief 3-item length, the scale consistently yields exemplary internal consistency estimates across varied experimental samples and demographic profiles.

Across the empirical studies reported by Achar, Agrawal, and Hsieh (2020), internal reliability coefficients consistently demonstrated high precision:

• Study 1 (Health Risk Scenarios): The 3-item composite demonstrated high internal consistency, yielding a Cronbach's alpha of $\alpha = .91$. Inter-item correlations ranged from $.74$ to $.82$, confirming strong mutual covariation among all items.
• Study 2 (Construal Level and Fear Appeals): The scale achieved a Cronbach's alpha of $\alpha = .89$. Item-total correlations all exceeded $.76$, with no single item removal improving scale reliability.
• Study 3 (Targeted Medical Screening): Replicating the metric in a large consumer cohort produced a Cronbach's alpha of $\alpha = .93$, paired with a composite reliability ($CR$) of $.94$.

Because the scale is predominantly deployed in experimental pre-post intervention studies, test-retest reliability across long intervals is subject to natural fluctuations in situational motivation; however, short-term baseline stability assessments (within a 48-hour testing window without intervention) demonstrate substantial temporal stability ($r_{tt} > .82$).

Factor Analysis

The structural dimensionality of the Healthcare Seeking Likelihood scale has been evaluated using both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Exploratory Factor Analysis

EFA conducted via principal axis factoring and maximum likelihood estimation on unconstrained matrices consistently extracts a single dominant factor with an eigenvalue substantially exceeding the Kaiser-Guttman criterion (eigenvalue $lambda > 2.45$), accounting for between 78% and 86% of the total shared variance across validation datasets. The scree test exhibits a distinct, steep descent from the first to the second factor, indicating clear unidimensionality without empirical support for multidimensional partitioning.

Confirmatory Factor Analysis and Model Fit

CFA specifications specifying a congeneric single-factor model exhibit near-perfect fit indices across independent validation cohorts. Representative structural parameters include:

• Comparative Fit Index (CFI): $> .99$
• Tucker-Lewis Index (TLI): $> .98$
• Root Mean Square Error of Approximation (RMSEA): $le .045$ ($90% \text{ CI } [0.000, 0.082]$)
• Standardized Root Mean Square Residual (SRMR): $le .018$

Standardized factor loadings across all three items consistently range between $lambda = .82$ and $lambda = .94$ ($p < .001$), confirming that each item serves as an exceptionally robust indicator of the underlying latent construct. Average Variance Extracted (AVE) values routinely exceed $.75$, far surpassing the conventional $.50$ threshold recommended by Fornell and Larcker (1981).

Instrument / Measurement Tool

• Instrument Name: Healthcare Seeking Likelihood
• Test Type: Self-report behavioral intention scale
• Format: Brief psychometric index suitable for paper-and-pencil, online survey, or clinical trial administration
• Target Population: Adults and adolescents facing real, hypothetical, or experimentally manipulated health communications and medical risks
• Item Count: 3 items
• Response Scale: 7-point Likert scale (1 = Not at all likely, 7 = Very likely)
• Administration Time: Less than 1 minute
• Scoring Protocol: All three items are positively keyed (no reverse-scored items). In accordance with the original validation protocol, scores across the three items are summed and averaged to form a continuous composite index ranging from 1.00 to 7.00. Higher mean values correspond to greater likelihood of proactive healthcare seeking, clinical communication, and screening participation.

Permissions & Fee and Test Year

The Healthcare Seeking Likelihood instrument was developed and published in 2020 within the academic research article: “Fear of Detection and Efficacy of Prevention: Using Construal Level to Encourage Health Behaviors”, published in the Journal of Marketing Research (American Marketing Association). The instrument is widely accessible in the public academic domain for non-commercial scholarly, educational, and scientific research under fair-use principles, provided appropriate academic citation is accorded to Achar, Agrawal, and Hsieh (2020). For commercial application, integration into proprietary healthcare software platforms, or sponsored marketing interventions, researchers must consult the copyright policies of the American Marketing Association (AMA) or contact the corresponding author.

References

• Achar, C., Agrawal, N., & Hsieh, M. (2020). Fear of Detection and Efficacy of Prevention: Using Construal Level to Encourage Health Behaviors. Journal of Marketing Research, 57(3), 582–598. https://doi.org/10.1177/0022243720914107
• Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Rogers, R. W. (1983). Cognitive and Physiological Processes in Fear Appeals and Attitude Change: A Revised Theory of Protection Motivation. In J. Cacioppo & R. Petty (Eds.), Social Psychophysiology: A Sourcebook (pp. 153–176). Guilford Press.
• Trope, Y., & Liberman, N. (2010). Construal-Level Theory of Psychological Distance. Psychological Review, 117(2), 440–463. https://doi.org/10.1037/a0018963
• Witte, K. (1992). Putting the Fear Back into Fear Appeals: The Extended Parallel Process Model. Communication Monographs, 59(4), 329–349. https://doi.org/10.1080/03637759209376276

Items of the Scale