The Decision Evaluation Scales<\/strong> (DES) is a multidimensional, patient-reported psychometric instrument developed by Dr. Peep F. M. Stalmeier<\/a> and colleagues in 2005 at the Radboud University Medical Center in the Netherlands. Comprising 15 items, the DES is designed to retrospectively evaluate how patients perceive and appraise a medical decision following clinical consultations or interventions. Grounded in modern psychometric theory, the scale was constructed and refined using the Rasch measurement model<\/a>, yielding three psychometrically distinct, unidimensional subscales: Satisfaction-Uncertainty<\/em> (evaluating emotional resolution, lingering doubts, and confidence in the chosen path), Informed Choice<\/em> (assessing perceived adequacy of information, clarity of consequences, and understanding of treatment alternatives), and Decision Control<\/em> (measuring patient autonomy, personal ownership of the decision, and perceived influence relative to the healthcare provider). Unlike generic patient satisfaction questionnaires that suffer from pervasive ceiling effects, the DES distinguishes cognitive, affective, and relational elements of medical decision-making. The instrument demonstrates strong psychometric properties, with subscale Cronbach’s alpha<\/a> reliability coefficients consistently ranging between .78 and .88, favorable Rasch infit and outfit mean-square indices (0.7 to 1.3), and robust convergent validity against legacy instruments such as the Decisional Conflict Scale (DCS) and the Decision Regret Scale (DRS). The DES is extensively utilized in oncology, surgical decision-making, genetic counseling, and chronic disease management to evaluate shared decision-making<\/a> (SDM) interventions, patient decision aids (PtDAs), and communicative training for clinicians.<\/p>\n Decision Evaluation Scales, DES, shared decision-making, medical decision-making, Rasch measurement model, informed choice, decision control, patient-reported outcome measures, decisional conflict, health psychology, psychometrics, patient autonomy, clinical decision aids<\/p>\n The Decision Evaluation Scales were developed and validated by an interdisciplinary research team specializing in medical decision sciences, psychometrics, and clinical oncology at the Radboud University Medical Center (Radboudumc) in Nijmegen, The Netherlands:<\/p>\n Modern clinical practice has shifted from historical paternalism toward collaborative paradigms of healthcare, centered on patient autonomy and shared decision-making. While the conceptual benefits of shared decision-making are widely acknowledged, accurately quantifying how patients experience, process, and evaluate critical medical choices has presented persistent methodological challenges. The Decision Evaluation Scales (DES) were engineered to provide an empirically rigorous, psychometrically robust tool to assess post-decisional cognitive appraisals, affective responses, and structural dynamics of the patient-clinician encounter.<\/p>\n Traditional evaluation metrics in healthcare environments have frequently relied on broad patient satisfaction<\/a> surveys. These traditional instruments exhibit substantial limitations, most notably severe ceiling effects, where 80% to 95% of respondents indicate high satisfaction despite possessing low clinical knowledge, experiencing severe unexpressed anxiety, or harboring unvoiced ambivalence. The primary purpose of the DES is to overcome these ceiling effects by decomposing post-decisional appraisal into distinct latent dimensions that separate emotional comfort from intellectual clarity and relational autonomy.<\/p>\n In clinical practice, the DES serves as an evaluative and diagnostic instrument. Clinicians and care teams can administer the instrument following complex consultations\u2014such as elective surgical choices, choices between mastectomy versus breast-conserving therapy, systemic chemotherapy regimens, genetic testing for hereditary cancer syndromes, or palliative care transitions. By inspecting individual subscale scores, healthcare providers can identify specific deficits in patient care. For instance, a patient might demonstrate high scores on Decision Control<\/em> and Satisfaction-Uncertainty<\/em>, yet score poorly on Informed Choice<\/em>, indicating that while the patient felt autonomous and emotionally reassured, their comprehension of treatment tradeoffs was suboptimal, signaling a need for additional educational reinforcement.<\/p>\n In academic research, the DES serves as a primary outcome measure for randomized controlled trials (RCTs) evaluating patient decision aids, communicative training interventions for healthcare practitioners, and structured decision-support algorithms. By employing a Rasch-validated metric, researchers obtain interval-level measurement properties that allow for linear comparisons across heterogeneous cohorts, longitudinal assessment of decisional durability over time, and comparative effectiveness research across diverse clinical specialties.<\/p>\n The Decision Evaluation Scales measure the overarching construct of post-decisional evaluation<\/strong>, conceptualized as a retrospective, multifaceted cognitive and affective appraisal of a healthcare choice. Rather than viewing decision evaluation as a global, unitary sentiment, the psychometric architecture of the DES posits that patients experience three distinct, interrelated dimensions:<\/p>\n This subscale captures the affective equilibrium and psychological comfort of the patient regarding the choice made. It encompasses the resolution of decisional ambivalence, feelings of post-decisional confidence, relief, and the absence of cognitive dissonance or lingering doubt. In psychometric modeling, this construct spans a continuum from intense decisional distress, hesitation, and second-guessing at the lower end, to unequivocal decisional peace, resolution, and perceived correctness at the higher end.<\/p>\n Clinical Example:<\/em> Following a consultation regarding whether to initiate adjuvant chemotherapy, a patient with high scores on this dimension feels emotionally settled, expresses confidence that the selected course aligns with their personal life values, and experiences minimal internal conflict or dread about whether an alternative option would have been superior.<\/p>\n The Informed Choice subscale isolates the epistemic and cognitive dimension of the decision. It assesses the degree to which the patient perceives that they received sufficient, high-quality, and balanced medical information regarding all available alternatives, including watchful waiting. This construct evaluates the patient’s clarity concerning the pros, cons, potential side effects, long-term prognostic outcomes, and trade-offs inherent in each clinical pathway.<\/p>\n Clinical Example:<\/em> In prostate cancer screening or treatment selection (e.g., active surveillance vs. radical prostatectomy), a patient scoring high on Informed Choice understands the probabilities of urinary incontinence, erectile dysfunction, and biochemical recurrence across both pathways, feeling that their eventual choice was built on comprehensive, transparent medical evidence rather than clinical ambiguity.<\/p>\n The Decision Control dimension measures perceived autonomy, agency, and relational self-determination within the clinical interaction. Grounded in conceptual models of patient participation, this construct evaluates the balance of power between patient and clinician. It differentiates between passive compliance (where the patient feels the decision was imposed or predominantly directed by the physician) and active self-determination or collaborative partnership (where the patient feels their preferences were central to the final resolution).<\/p>\n Clinical Example:<\/em> When managing chronic degenerative joint disease, a patient evaluating total hip arthroplasty who demonstrates elevated Decision Control reports that they retained final sovereignty over the timing and selection of surgery, feeling empowered rather than coerced or bypassed by the orthopedic surgeon’s clinical preference.<\/p>\n The conceptual and methodological architecture of the Decision Evaluation Scales integrates three theoretical frameworks from cognitive psychology, behavioral medicine, and psychometrics:<\/p>\n The historical baseline for decisional appraisal derives from Irving Janis<\/a> and Leon Mann’s (1977) Conflict Model of Decision Making<\/em>, alongside Leon Festinger’s<\/a> (1957) theory of Cognitive Dissonance<\/em>. Janis and Mann posited that vital decisions involving health risks trigger substantial psychological stress, characterized by vacillation, apprehension, and anticipatory regret. When individuals make difficult choices involving uncertain trade-offs, post-decisional dissonance frequently emerges if the negative attributes of the chosen alternative or the positive attributes of the rejected alternatives become salient. The DES formalizes this cognitive-affective post-decisional state, operationalizing the extent to which clinical communication successfully facilitates cognitive closure, minimizes dissonance, and mitigates post-decisional distress.<\/p>\n The DES is anchored in modern models of shared decision-making formulated by Charles, Gafni, and Whelan (1997) and operationalized by Glyn Elwyn and colleagues (2012). These models stipulate that high-quality clinical decisions require three foundational conditions: choice awareness (acknowledging that a choice exists), option comparison (understanding the comparative risks and benefits), and preference elicitation (integrating patient values into the final selection). The three subscales of the DES map onto this paradigm:<\/p>\n Unlike instruments constructed solely through Classical Test Theory (CTT), the DES was built on the Rasch measurement model<\/a> formulated by Danish mathematician Georg Rasch (1960). Modern psychometricians emphasize that ordinal Likert scores should not be treated as linear interval measures without mathematical justification. Rasch modeling formalizes the probability of a specific category response as a logistic function of the difference between a person’s latent trait level ($\\theta_n$<\/span>) and the item’s calibration location ($b_i$<\/span>):<\/p>\n $$P(X_{ni} = 1) = \\frac{\\exp(\\theta_n – b_i)}{1 + \\exp(\\theta_n – b_i)}$$<\/span><\/p>\n By fitting empirical item responses to the Rasch model, Stalmeier et al. ensured invariant item ordering, verified unidimensionality for each subscale, confirmed that item difficulties span the latent trait evenly, and established that individual subscale raw scores can be legitimately converted into linear interval scales free from local item dependency.<\/p>\n The Decision Evaluation Scales have undergone psychometric validation across multiple clinical settings, including clinical genetics, oncology, surgical subspecialties, and reproductive medicine. The evidence for validity spans several domains:<\/p>\n The initial item pool for the DES was derived from qualitative interviews with patients facing difficult clinical decisions, extensive reviews of existing decisional instruments, and expert panels comprising decision analysts, oncologists, medical ethicists, and health psychologists. Rasch item fit diagnostics were iteratively utilized to eliminate ambiguous, redundant, or poorly functioning items, ensuring that the final 15 items represent distinct, clinically meaningful facets of the three core domains without cognitive overload.<\/p>\n Convergent validity has been evaluated against legacy instruments measuring decisional quality and distress:<\/p>\n Discriminant validity was established by comparing DES dimensions against non-decisional psychological constructs, including general health-related quality of life (SF-36<\/a> physical functioning subscale) and baseline demographic parameters (age, educational attainment, socioeconomic status). As hypothesized, correlations between DES subscales and general physical functioning are negligible to weak ($r = .08$<\/span> to $.18$<\/span>), demonstrating that the DES evaluates specific decisional processes rather than general health state or functional impairment.<\/p>\n Known-groups validation studies show that the DES distinguishes between patients who utilize structured Patient Decision Aids (PtDAs) versus those receiving routine clinical care. In controlled trials, patients provided with balanced PtDAs score significantly higher on the Informed Choice<\/em> subscale ($d = 0.54, p < .01$<\/span>) and display lower uncertainty on the Satisfaction-Uncertainty<\/em> scale ($d = 0.42, p < .05$<\/span>). Furthermore, higher scores on Decision Control<\/em> predict higher treatment adherence and lower rates of post-surgical decisional regret over long-term clinical follow-up.<\/p>\n The reliability of the Decision Evaluation Scales has been established using both Classical Test Theory metrics (internal consistency and test-retest coefficients) and Item Response Theory \/ Rasch indices (person separation reliability and item separation indices):<\/p>\n Empirical investigations across diverse patient samples demonstrate strong internal consistency for the three Rasch-calibrated subscales:<\/p>\n Because traditional Cronbach’s alpha assumes tau-equivalence and unbounded continuous scales, the Rasch reliability indices provide more rigorous evidence of measurement stability:<\/p>\n In stable patient cohorts re-assessed across a 2- to 3-week interval without intervening clinical developments, intraclass correlation coefficients (ICC<\/a>) confirmed satisfactory reproducibility:<\/p>\n The structural integrity of the 15-item Decision Evaluation Scales was evaluated through both exploratory\/confirmatory factor analytic approaches and modern probabilistic Rasch modeling.<\/p>\n During scale construction, item suitability was evaluated using Georg Rasch’s dichotomous and polytomous rating scale models. Item fit was determined using Mean Square (MNSQ<\/a>) residual statistics:<\/p>\n To verify the unidimensionality of each independent subscale, Principal Component Analysis of Rasch Residuals was performed. For each of the three subscales:<\/p>\n When examined under a Classical Test Theory framework, a three-factor oblique structural equation model confirms the theoretical configuration over a unidimensional or two-factor alternative. Fit indices from confirmatory factor analytic investigations report:<\/p>\n The Decision Evaluation Scales is a standardized, self-report instrument designed for either pen-and-paper or digital administration. Below are the administrative parameters and structural characteristics of the tool:<\/p>\n The Decision Evaluation Scales was officially published in 2005<\/strong> by Dr. Peep F. M. Stalmeier and colleagues at the Radboud University Medical Center (Nijmegen, Netherlands). As an academic instrument developed through public research funding, the DES is generally made accessible free of charge for non-commercial academic research, clinical quality improvement initiatives, and educational use.<\/p>\n However, users must adhere to ethical standards and copyright conventions:<\/p>\n Below is a curated list of peer-reviewed literature and theoretical foundations associated with the Decision Evaluation Scales:<\/p>\n The official Decision Evaluation Scales (DES) consists of 15 proprietary items developed by Stalmeier et al. (2005), calibrated via the Rasch measurement model into three distinct subscales. To respect intellectual property and maintain test integrity, the complete verbatim operational questionnaires (available in Dutch and validated English translations) should be acquired from the original authors or published repository via Radboud University Medical Center.<\/p>\n Below is a conceptual representation illustrating the structural distribution, target constructs, response format, and scoring directionality across the 15 items:<\/p>\n Respondents rate each statement reflecting on their recent medical decision using a 5-point Likert agreement scale:<\/p>\n Focus: Affective evaluation, post-decisional peace, absence of doubt, confidence in the selection.<\/em><\/p>\n Focus: Cognitive evaluation, perceived adequacy of information, clarity of consequences and alternatives.<\/em><\/p>\n Focus: Relational and procedural evaluation, personal agency, perceived influence relative to the clinician.<\/em><\/p>\n The Decision Evaluation Scales (DES), developed by Stalmeier et al. (2005), is a 15-item Rasch-validated psychometric questionnaire evaluating patient appraisals of medical decisions across Satisfaction-Uncertainty, Informed Choice, and Decision Control.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","categories":[9829,109,606,2],"tags":[9830,3647,206,9832,9831,9833,8512,164],"class_list":["post-2588","psych_scale","type-psych_scale","status-publish","hentry","category-decision-making-scales","category-health-psychology","category-medical-psychology","category-psychometrics","tag-decision-evaluation-scales","tag-des","tag-health-psychology","tag-informed-choice","tag-medical-decision-making","tag-patient-autonomy","tag-rasch-measurement-model","tag-shared-decision-making"],"_links":{"self":[{"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/psych_scale\/2588","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/psych_scale"}],"about":[{"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/types\/psych_scale"}],"author":[{"embeddable":true,"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ashadocs.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2588"}],"version-history":[{"count":0,"href":"https:\/\/ashadocs.org\/index.php?rest_route=\/wp\/v2\/psych_scale\/2588\/revisions"}],"wp:attachment":[{"href":"https:\/\/ashadocs.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2588"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ashadocs.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2588"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ashadocs.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2588"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}2. Keywords<\/h2>\n
3. Authors<\/h2>\n
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4. Purpose<\/h2>\n
5. Psychological Construct<\/h2>\n
1. Satisfaction-Uncertainty (Tevredenheid-Onzekerheid)<\/h3>\n
2. Informed Choice (Ge\u00efnformeerde keuze)<\/h3>\n
3. Decision Control (Beslissingscontrole)<\/h3>\n
6. Theoretical Framework<\/h2>\n
Decisional Conflict Theory and Cognitive Dissonance<\/h3>\n
The Shared Decision-Making Framework<\/h3>\n
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The Rasch Measurement Model (Modern Psychometric Theory)<\/h3>\n
7. Validity<\/h2>\n
Content and Face Validity<\/h3>\n
Construct and Convergent Validity<\/h3>\n
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Discriminant Validity<\/h3>\n
Predictive and Known-Groups Validity<\/h3>\n
8. Reliability<\/h2>\n
Internal Consistency<\/h3>\n
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Rasch Reliability and Separation Indices<\/h3>\n
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Test-Retest Stability<\/h3>\n
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9. Factor Analysis & Rasch Calibration<\/h2>\n
Rasch Analysis and Infit\/Outfit Diagnostics<\/h3>\n
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Principal Component Analysis of Residuals (PCAR)<\/h3>\n
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Confirmatory Factor Analysis (CFA) Fit Indices<\/h3>\n
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10. Instrument \/ Measurement Tool<\/h2>\n
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\n $$\\text{Standardized Score} = \\left( \\frac{\\text{Mean Raw Score} – 1}{4} \\right) \\times 100$$<\/span>\n <\/li>\n11. Permissions & Fee and Test Year<\/h2>\n
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12. References<\/h2>\n
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13. Items of the Scale<\/h2>\n
Response Format<\/h3>\n
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Subscale 1: Satisfaction-Uncertainty (Tevredenheid-Onzekerheid)<\/h3>\n
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Subscale 2: Informed Choice (Ge\u00efnformeerde keuze)<\/h3>\n
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Subscale 3: Decision Control (Beslissingscontrole)<\/h3>\n
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