Understanding cognitive errors in clinical decision-making: a Q methodology approach

Abstract

Background

Diagnostic errors are a significant cause of patient harm and legal issues in medicine, with the majority being cognitive in nature. This study aimed to explore the key elements that define these cognitive error styles among pediatric medical trainees (including interns, residents, and fellows). We specifically focused on characteristics of the decision maker that have not been adequately described previously in the literature but are highly relevant to clinical decision making (CDM).

Methods

Using Q-methodology, a research approach that combines qualitative and quantitative techniques to study human subjectivity, we examined the personal profiles of trainees regarding cognitive errors. Through a systematic analysis of 58 statements sorted by 48 participants from a pediatric referral hospital in Isfahan, Iran, we identified eight distinct patterns of cognitive error. These eight factors accounted for 66%of the variance in perspectives.

Results

Eight distinct cognitive error profiles were tentatively identified among the participants. The profiles were labeled as “Pressure & Authority-Driven Reasoner” (19%), “Self-Oriented Reasoner” (13%), “Prototype-Driven Matcher” (10%), “Fear- and Pressure-Anchored Ignoring” (7%), “Availability- and Experience-Based Reasoner” (6%), “Premature Closure Satisficing Reasoner” (5%), “Diagnostic Momentum with Selective Cue Rejection” (4%), and “Anchoring-Driven Diagnostic Rigidity” (4%). Preliminary observations suggest the “Availability and Authority-Deference” profile may be more associated with younger participants, while some experienced students were associated with the “Anchored and Stubborn” profile.

Conclusions

These findings tentatively indicate that diagnostic errors arise from diverse, interacting cognitive styles and personality characteristics rather than uniform deficits in knowledge. Identifying distinct diagnostic reasoning profiles highlights the need for developmentally informed educational strategies.

Introduction

Diagnostic errors represent one of the most serious and preventable threats to patient safety. A notable report by the U.S. Agency for Healthcare Research and Quality (AHRQ) estimates that over seven million diagnostic errors occur annually in U.S. emergency departments, of which approximately 350,000 result in serious harm and 250,000 in death [1]. These staggering figures emphasize that diagnostic error is not merely a technical or procedural failure but a deeply cognitive phenomenon rooted in the complex processes of clinical reasoning. Cognitive errors arising from biases in perception, attention, and judgment constitute a substantial proportion of diagnostic failures and often have more profound consequences than system or communication errors [1–3].

A growing body of literature identifies cognitive biases as systematic, unconscious, and automatic patterns of thought that distort judgment and decision making in clinical settings [4–7]. The dual-process theory provides a foundational explanation for how such biases occur. According to this framework, human reasoning operates through two interacting systems: system 1, which is fast, intuitive, and heuristic-driven, and system 2, which is slow, analytical, and deliberate [8–10]. While System 1 enables clinicians to make efficient judgments under time pressure, its reliance on heuristics makes it vulnerable to bias in atypical or ambiguous cases. For instance, clinicians may anchor prematurely on an initial diagnosis or interpret subsequent data through a confirmatory lens. Conversely, system 2 thinking, although more deliberate, can be undermined by factors such as overconfidence, cognitive overload, and hierarchical deference. Consequently, both systems exhibit inherent vulnerabilities that can predispose clinicians to diagnostic errors [10].

Cognitive errors in clinical decision-making have been categorized into six major groups encompassing diagnostic fixation (e.g., anchoring, confirmation bias, premature closure), failure to consider alternatives (e.g., representativeness restraint, search satisfaction), inherited reasoning (e.g., diagnostic momentum, framing effects), misjudgment of prevalence or probability (e.g., availability bias, base-rate neglect), patient- or context-related biases (e.g., fundamental attribution error, visceral bias), and affective or personality-related tendencies (e.g., overconfidence, omission bias, commission bias) [11, 12]. While these taxonomies have advanced conceptual clarity, they largely treat biases as universal cognitive phenomena rather than as expressions of individual tendencies.

Essentially, this means that the personality characteristics of the decision maker have not previously been assessed in the CDM literature, even though they appear fundamentally relevant [13]. Clinicians differ markedly in how they perceive, rationalize, and manage their own cognitive vulnerabilities. These differences may form distinctive cognitive error styles. Furthermore, characteristics such as attachment styles represent an area of CDM that has not been sufficiently investigated [14].Attachment styles likely influence how a clinician interacts with uncertainty, patient demands, and hierarchical pressures. It should be noted that any assessment of decision-making could also be confounded by the interacting attachment styles of both the patient (or their caregivers) and the decision maker [15].

In reality, clinicians differ markedly in how they perceive, rationalize, and manage their own cognitive vulnerabilities. These differences may form distinctive cognitive error styles habitual patterns through which certain types of biases are more likely to manifest in an individual’s reasoning. Cognitive error style can thus be viewed as the personal “signature” of a clinician’s diagnostic thinking, shaped by experience, emotional disposition, situational awareness, and contextual factors. Understanding such styles offers valuable insights for tailoring educational strategies, promoting reflective practice, and designing personalized cognitive debiasing interventions [12, 16, 17].

The pediatric environment provides a particularly revealing context for studying these issues. Pediatricians often operate in fast-paced, emotionally charged, and high-stakes clinical settings where cognitive shortcuts are both necessary and risky [16–19]. Evidence from patient and clinician surveys indicates that a substantial proportion of pediatric patients and families have experienced diagnostic errors, and many pediatricians report encountering them on a monthly basis [20]. These findings underscore the vulnerability of pediatric decision making processes to human cognitive limitations. Moreover, the distinctive features of pediatric care such as indirect communication through caregivers, rapidly evolving patient conditions, and developmental variability may further complicate reasoning and bias awareness [6].

Despite increasing recognition of diagnostic errors in pediatrics, limited research has examined how pediatricians themselves conceptualize their own cognitive tendencies and vulnerabilities. Most existing studies rely on retrospective chart reviews, incident reports, or quantitative surveys that fail to capture the subjective dimension of clinical reasoning the ways in which physicians understand, interpret, and reflect upon their own biases. Exploring pediatricians’ perspectives can illuminate the diversity of cognitive styles underlying diagnostic decisions and provide a foundation for more targeted educational and quality-improvement initiatives [2, 16, 20].

To investigate these highly subjective characteristics, we utilized Q-methodology. Q-sort is a robust approach specifically designed to capture subjective viewpoints by having participants rank a set of statements relative to one another. It is being used here because it bridges qualitative exploration and quantitative factor analysis, allowing us to systematically identify shared patterns of thought or “profiles” across individuals while respecting the complexity of their personal psychology [21–25]. Therefore, this study aims to answer the question: “What are pediatricians’ viewpoints on their cognitive error style?”. Through this inquiry, we seek to map the subjective landscape of cognitive error styles among pediatricians and to provide an empirically grounded understanding that can inform future interventions in diagnostic education and patient safety.

Materials and methods

Study design

This study used Q-methodology to identify and compare pediatric trainees’ subjective viewpoints on cognitive error tendencies in clinical reasoning. Q-methodology integrates qualitative exploration with quantitative factor analysis and is specifically designed to map shared patterns thought among participants [21–27]. Participants rank a systematically developed set of statements (Q-set), and the resulting Q-sorts undergo inverted factor analysis to identify clusters of similar viewpoints [27, 28].

Setting and participants

The study was conducted at the Pediatric Hospital of Isfahan University of Medical Sciences, a tertiary referral teaching center. Trainees at three educational levels (interns, residents, and fellows) were recruited through convenience sampling between September 2024 and January 2025. Q-methodology prioritizes diversity of perspectives rather than statistical representativeness; recommendations suggest that 30–60 participants yield stable factor solutions [27, 29]. A total of 48 trainees (7 fellows, 21 residents, 20 interns) completed the Q-sort. Participants ranged in age from 27 to 62 years (31 female, 17 male).

Phase 1: Q-set development

The development of the Q-set followed a multi-stage, iterative process consistent with best-practice Q-methodology:

Step 1: literature review

A comprehensive literature review was conducted to identify cognitive errors, existing measurement tools, and previously reported prevalence in clinical practice. Multiple studies were compared, and limitations of existing instruments particularly their narrow coverage and lack of pediatric applicability were identified. Studies reporting common cognitive errors in pediatric settings were compared [30], and the most frequently documented errors within six major cognitive-error domains were selected as candidates for initial item formation [31].

Step 2: item drafting and conceptual refinement

Based on the extracted error categories, one to three representative cognitive errors per domain were selected. Drawing on definitions, examples, and case-based descriptions from the literature, the research team held six structured meetings to conceptualize each error and generate an initial pool of 116 Q-statements. Statements were drafted to be concise, clear, and unambiguous, each reflecting a single cognitive-error construct.

Step 3: semi-structured interviews

To ensure contextual relevance, semi-structured interviews were conducted with 39 trainees (24 interns, 12 residents, 3 fellows). Interviews (40–60 min) probed participants’ experiences with personal or observed diagnostic errors. Transcripts were analyzed using deductive content analysis based on the six cognitive-error categories. A total of 104 codes were identified and compared to the draft items, supporting the refinement and confirmation of the 116 preliminary Q-statements.

Step 4: expert review and qualitative content validation

In this phase, the 116 statements underwent content evaluation by ten experts (five medical education specialists and five psychologists). Reviews focused on clarity, redundancy, and conceptual alignment. Based on feedback, two new statements were added, and two categories (Sunk Cost and Hindsight Bias) were removed due to insufficient relevance in pediatrics. After wording revisions and removal of overlapping items, 64 statements remained.

Step 5: refinement of the Q-statement pool

In Phase 3, the research team conducted four structured two-hour meetings to evaluate the relevance, overlap, and completeness of the initial 116 statements. Interview transcripts and extracted codes were rechecked to ensure alignment between empirical data and literature-derived statements. Wording was revised for clarity, simplicity, and suitability to the target group. After removing redundancies and resolving inconsistencies, the pool was reduced to 109 refined statements.

Step 6: content and face validity assessment

Five medical education experts and five psychology experts reviewed the 109 statements for necessity, clarity, and coherence. Their feedback resulted in the addition of two statements, removal of two cognitive error categories (Sunk Cost, Hindsight Bias), and several wording revisions, yielding 64 statements.

Using Lawshe’s method, five diagnostic reasoning experts rated each of the 64 items. Each statement was rated on a 3-point scale for necessity, and the Content Validity Ratio (CVR) and Content Validity Index (CVI) were calculated using SPSS [32]. Items not meeting the minimum CVR threshold (0.99) were removed, resulting in the deletion of five statements. The remaining items demonstrated a CVI of 1.0.

Face validity was assessed through cognitive interviews with two clinical experts and five medical students [33]. Three statements were revised and one was removed, producing a final set of 58 Q-statements.

Phase2: data collection and Q-sorting procedure

Data collection was conducted using an in-person, computer-assisted Q-sorting procedure, following established Q-methodology guidelines [34–36]. All Q-sorts were completed digitally on the QTIP online platform (University of Wisconsin–Madison), which provides a browser-based drag-and-drop interface. Each participant completed the sorting individually in a quiet room.

Participants first received a standardized explanation regarding the study objectives, Q-methodology structure, and the −5to +5quasi-normal distribution (Fig. 1). To provide a clear context for their judgments, participants were given a specific “Condition of Instruction” (the sorting question). They were asked to sort the statements based on the following prompt: “Which of these statements most accurately describes your own typical thought processes and vulnerabilities when you reflect on diagnostic errors you have experienced or narrowly avoided?” Therefore, when agreeing with a statement, they were indicating its relevance to their personal diagnostic reasoning style [24, 34, 35].

Fig. 1.

Grid showing the prearranged frequency distribution for the Q-sorting process

Initially, participants performed a triadic pre-sort of all 58 statements into “agree,” “disagree,” and “neutral/uncertain” categories, a step recommended to reduce cognitive load [34, 35]. Statements were then allocated across an 11-column quasi-normal distribution on QTIP using the drag-and-drop interface. Digital Q-sorting has been shown to be equivalent to traditional paper-based methods and produces valid factor-analytic outputs. Sorting sessions typically required 40–50 min. Upon completion, the researcher and participant verified the final arrangement to ensure accuracy and completeness. QTIP automatically saved each Q-sort and generated a unique anonymized identifier. Immediately afterward, participants completed a demographic questionnaire (age, gender, training level, and years of clinical experience), administered post-sort to minimize priming effects [24, 34] (Fig. 1).

Phase3: data export and data analysis

All Q-sorts were exported from QTIP in a PQMethod-compatible format and subsequently imported into PQMethod 2.35 for by-person factor analysis. This analytic approach adhered to standard Q-methodological procedures, including centroid factor extraction and Varimax rotation, as recommended by canonical texts [24, 35]. The Q-sorts were collected digitally using qmethodsoftware.com, an established online survey platform [36].

• Correlation Matrix: Inter-correlations among all Q-sorts were computed to assess similarities in participants’ viewpoints.

• Factor Extraction: Principal Component Analysis (PCA) was applied, followed by Varimax rotation.

• Factor Retention: Factors were retained based on eigenvalues > 1.0, stability across participants, Humphrey’s rule for significance, and interpretability.

• Factor Interpretation: Participants with significant factor scores (p < 0.01) were used to construct factor arrays. Distinguishing and consensus statements were identified for each viewpoint.

Ethical considerations

Ethical approval was obtained from the Institutional Review Board of Isfahan University of Medical Sciences (Ref: 3401125-IR.ARI.MUI.REC.1401.080). Participation was voluntary, and data were collected anonymously. All procedures conformed to the Declaration of Helsinki (2013).

Quality assurance and transparency

To ensure reproducibility, all Q-sorts, correlation matrices, and factor arrays are documented in Tables 1, 2, 3 and 4, allowing explicit verification by reviewers. Analytic procedures followed canonical Q-methodology guidelines and contemporary best practices for factor interpretation [35].

Table 1.

Demographic characteristics of participants

Variable	Category	n (%)
Gender	Female	31 (64.6)
	Male	17(35.4)
Training Level	Intern	20 (41.7)
	Resident	21 (43.7)
	Fellow	7 (14.6)
Age (years)	30.77 ± 6.34	48
Work experience	3.35 ± 6.01	48

Table 2.

Summary of extracted factors

Factor	Eigenvalue	% Variance	Cumulative % Expln Var	Humphrey’s Rule	Standard Error	No. of Significant score
1	8.89	19	19	0.51	0.14	3
2	6.34	13	32	0.58	0.14	8
3	4.68	10	41	0.46	0.14	6
4	3.30	7	48	0.35	0.14	2
5	2.69	6	54	0.27	0.14	2
6	2.28	5	59	0.18	0.14	4
7	1.98	4	63	0.21	0.14	2
8	1.69	4	66	0.19	0.14	2

Table 3.

Example of factor array (Extract: top 7 and bottom 7 statements for each factor)

Factor	Most Representative (+ 5 / +4)	Least Representative (–5 / − 4)
1	Early diagnosis of the disease due to limitations such as crowded ward and pressure from companions(+ 5) Highlighting data in favor of the initial diagnosis in patient presentation(+ 5) Over-reliance on the first information received about the patient(+ 5) Not conducting other additional investigations after the first diagnosis that came to mind (+ 4) Focusing on previous diagnoses, suggested by others (Expert or residents) (+ 4) Ignoring further evidence and symptoms after reaching a satisfactory diagnosis(+ 4) Diagnosis for the patient based on previous lasting experiences(+ 4)	Not questioning the diagnosis in the file(–5) Ignoring opinions that disagree with the initial diagnosis(–5) Believing in one’s own ability to reach a diagnosis without consulting others(–5) Following the patient’s self-labeling(–4) Performing diagnostic/therapeutic measures for the patient without indication(–4) Decision-making for the patient under treatment: How others (assistant, intern, etc.) present the patient as an unsatisfactory patient (–4) Unwillingness to search for evidence that contradicts the initial diagnosis(–4)
2	Believing in one’s own ability to assess patient problems (+ 5) Believing in being more capable of diagnosing illness than other physicians(+ 5) Performing diagnostic/therapeutic measures for the patient due to avoiding legal issues(+ 5) Experienced extensive patient exposure and overconfidence (+ 4) Performing a measure Diagnostic/therapeutic due to concerns of being labeled as an indifferent physician(+ 4) Relying on one’s own knowledge and skills to make decisions(+ 4) Performing diagnostic/therapeutic measures due to fear of losing the patient(+ 4)	Ignoring clinical findings that challenged the initial diagnosis(–5) Ignoring alternative diagnoses(–5) Ignoring new information that contradicts the first possible diagnosis(–5) Not conducting other additional investigations after the first diagnosis that came to mind (–4) Early confirmation of the diagnosis due to intolerance of ambiguity(–4) Accepting the first diagnosis before collecting more information(–4) Following the patient’s self-labeling(–4)
3	Emphasis on the age prevalence of the disease and the occurrence of symptoms to reach a diagnosis (+ 5) Relying on the similarity of symptoms with typical findings for diagnosis(+ 5) Decision-making for the patient under treatment: How others (residents, interns, etc.) present the patient’s overall condition as favorable(+ 5) Decision-making for the patient under treatment: How others (assistant, intern, etc.) present the patient’s overall condition as poor or unfavorable (+ 4) Emphasis on the conformity of findings with a specific stereotype without considering unusual manifestations Disease(+ 4) Early confirmation of the diagnosis due to intolerance of ambiguity(+ 4) Tendency to ignore unusual findings in diagnosing the disease(+ 4)	Ignoring clinical findings that challenged the initial diagnosis (–5) Unwillingness to search for evidence that contradicts the initial diagnosis (–5) Ignoring new information that contradicts the first possible diagnosis(–5) Not conducting other additional investigations after the first diagnosis that came to mind(–4) Overemphasizing the first possible diagnosis(–4) Ignoring opinions that disagree with the initial diagnosis(–4) Confidence in the correctness of the initial diagnosis(–4)
4	Ignoring opinions that disagree with the initial diagnosis (+ 5) Performing diagnostic/therapeutic measures due to fear of losing the patient(+ 5) Ignoring clinical findings that challenged the initial diagnosis(+ 5) Not paying attention to the patient’s age and sex in diagnosing the disease(+ 4) Ignoring the pre-test probability in diagnosing the disease(+ 4) Overemphasizing the first possible diagnosis(+ 4) Early diagnosis of the disease due to limitations such as crowded ward and pressure from companions(+ 4)	Stopping the search for more information once the evidence is sufficient and the diagnosis is satisfactory (–5) Ignoring further evidence and symptoms after reaching a satisfactory diagnosis(–5) Decision-making for the patient under treatment: How others (residents, interns, etc.) present the patient as a patient who is doing well (–5) Not questioning the diagnosis in the file(–4) Not seeking other treatments in pursuit of a sense of completeness of the chosen treatment(–4) Stopping the search for the cause of the disease symptoms once a plausible diagnosis is found(–4) Tendency to use a diagnostic/therapeutic method because of its use by other doctors(–4)
5	Choosing a diagnosis that was easily retrieved in the mind (+ 5) Diagnosis for the patient based on previous lasting experiences (+ 5) Ignoring the sensitivity and specificity of laboratory tests in diagnosing the disease (+ 5) Tendency to choose a diagnostic/therapeutic method based on the belief (adherence) of other doctors in this method (+ 4) Not paying attention to the patient’s age and sex in diagnosing the disease (+ 4) Ignoring the prevalence and incidence in diagnosing the disease (+ 4) Choosing a diagnostic/therapeutic method due to acceptance among colleagues (e.g. following a fashion) (+ 4)	Focusing on previous diagnoses, suggested by others (master or residents) (–5) Not questioning the diagnosis in the file (–5) Confidence in the correctness of the initial diagnosis(–5) Early diagnosis before clinical findings are complete(–4) Ignoring further evidence and symptoms after reaching a satisfactory diagnosis(–4) Ignoring other differential diagnoses due to the patient’s previous diagnosis(–4) Believing in one’s own ability to reach a diagnosis without consulting others(–4)
6	Early diagnosis of the disease due to limitations such as crowded ward and pressure from companions (+ 5) The impact of recent diagnostic experiences on the patient’s diagnosis (+ 5) Ignoring further evidence and symptoms after reaching a satisfactory diagnosis (+ 5) Ignoring other differential diagnoses due to the patient’s previous diagnosis(+ 4) Stopping the search for more information once the evidence is sufficient and the diagnosis is satisfactory(+ 4) Not seeking other treatments in pursuit of a sense of completeness of the chosen treatment(+ 4) Accepting the first diagnosis before collecting more information(+ 4)	Early confirmation of the diagnosis due to intolerance of ambiguity (–5) Tendency to make decisions for the patient with an emphasis on mortality rate(–5) Following the patient’s self-labeling(–5) Tendency to make decisions for the patient with an emphasis on survival rate (–4) Ignoring the prevalence and incidence in diagnosing the disease(–4) Achieving a diagnosis under the influence of the patient’s and his companions’ appearance and behavioral characteristics(–4) Decision-making for the patient under treatment: How others (assistant, intern, etc.) present the patient as an unsatisfactory patient (–4)
7	Performing diagnostic/therapeutic measures due to fear of losing the patient (+ 5) Focusing on previous diagnoses, suggested by others (master or residents) (+ 5) Decision-making for the patient based on recent cases(+ 5) Decision-making for the patient under treatment: How others (assistant, intern, etc.) present the patient as an unsatisfactory patient (+ 4) Decision-making for the patient under treatment: How others (residents, interns, etc.) present the patient as a patient who is doing well (+ 4) Over-reliance on the first information received about the patient(+ 4) Ignoring other differential diagnoses due to the patient’s previous diagnosis(+ 4)	Achieving a diagnosis under the influence of the patient’s and his companions’ appearance and behavioral characteristics (–5) Ignoring the cause of the disease and focusing on the patient’s and his companions’ personality and appearance characteristics(–5) Overemphasizing the patient’s individual and personality characteristics (obesity, race, ethnicity, etc.) to diagnose his disease(–5) Not paying attention to the patient’s age and sex in diagnosing the disease (–4) Ignoring the main cause of the disease and overemphasizing the patient’s and his companions’ cultural, social, and economic factors in diagnosing the disease (–4) Choosing a diagnostic/therapeutic method due to acceptance among colleagues (e.g. following a fashion) (–4) Tendency to make decisions for the patient with an emphasis on survival rate (–4)
8	Ignoring alternative diagnoses (+ 5) Confidence in the correctness of the initial diagnosis (+ 5) Difficulty in changing the initial diagnosis (+ 5) Overemphasizing the first possible diagnosis (+ 4) Over-reliance on the first information received about the patient(+ 4) Ignoring new information that contradicts the first possible diagnosis(+ 4) Accepting the first diagnosis before collecting more information (+ 4)	Unwillingness to search for evidence that contradicts the initial diagnosis (–5) Choosing a diagnosis that was easily retrieved in the mind(–5) Ignoring the sensitivity and specificity of laboratory tests in diagnosing the disease (–5) Relying on one’s own knowledge and skills to make decisions (–4) Overemphasizing the patient’s individual and personality characteristics (obesity, race, ethnicity, etc.) to diagnose his disease (–4) Not questioning the diagnosis in the file (–4) Ignoring the pre-test probability in diagnosing the disease (–4)

Table 4.

Sample distinguishing statements

Statement	Factor with Highest Agreement	Sort Values
Highlighting data in favor of the initial diagnosis in patient presentation	F1	(+ 5)
Believing in one’s own ability to assess patient problems Believing in being more capable of diagnosing illness than other physicians Performing diagnostic/therapeutic measures for the patient due to avoiding legal issues Experienced extensive patient exposure and overconfidence Performing a measure Diagnostic/therapeutic due to concerns of being labeled as an indifferent physician Relying on one’s own knowledge and skills to make decisions Tendency to use a diagnostic/therapeutic method because of its use by other doctors	F2	(+ 5) (+ 5) (+ 5) (+ 4) (+ 4) (+ 4) (+ 1)
Emphasis on the age prevalence of the disease and the occurrence of symptoms to reach a diagnosis Emphasis on the conformity of findings with a specific stereotype without considering unusual manifestations Disease Early confirmation of the diagnosis due to intolerance of ambiguity Tendency to ignore unusual findings in diagnosing the disease Following the patient’s self-labeling Overemphasizing the first possible diagnosis	F3	(+ 5) (+ 4) (+ 4) (+ 4) (+ 2) (–4)
Ignoring opinions that disagree with the initial diagnosis Ignoring clinical findings that challenged the initial diagnosis Ignoring the pre-test probability in diagnosing the disease Not seeking other treatments in pursuit of a sense of completeness of the chosen treatment Stopping the search for more information once the evidence is sufficient and the diagnosis is satisfactory	F4	(+ 5) (+ 5) (+ 4) (–4) (–5)
Ignoring the prevalence and incidence in diagnosing the disease Decision-making for the patient under treatment: How others (assistant, intern, etc.) present the patient as an unsatisfactory patient Focusing on previous diagnoses, suggested by others (master or residents)	F5	(+ 4) (+ 1) (–5)
Performing diagnostic/therapeutic measures due to fear of losing the patient	F6	(–3)
Ignoring the cause of the disease and focusing on the patient’s and his companions’ personality and appearance characteristics	F7	(–5)
Ignoring alternative diagnoses Ignoring new information that contradicts the first possible diagnosis Focusing on previous diagnoses, suggested by others (master or residents) The impact of recent diagnostic experiences on the patient’s diagnosis Choosing a diagnosis that was easily retrieved in the mind	F8	(+ 5) (+ 4) (–2) (–2) (–5)

Results

Participant characteristics

The study included 48 pediatric medical trainees, with a predominance of female participants (64.6%), reflecting the demographic composition of the training environment and warranting consideration in interpreting findings (Table 1). Trainees represented diverse educational stages: interns (41.7%), residents (43.7%), and fellows (12.6%), indicating a focus on early to mid-stage clinical experience. Mean age was 30.77 years (SD = 6.34), and mean work experience was 3.35 years (SD = 6.01), highlighting substantial heterogeneity in prior clinical exposure. Collectively, the demographic profile supports the potential generalizability of results when accounting for training stage and prior experience.

Correlation matrix

A full correlation matrix of 48 Q-sorts is presented in Appendix A. Inter-item correlations revealed coherent clusters of moderate-to-strong positive associations.The highest correlation value is 0.77, which is observed between variable 19 and variable 18 (correlation = 0.77) (positive) and − 0.71, which is observed between variable 5 and variable 4 (correlation = -0.71) (negative). The lowest correlation value is 0.11 (variables 1 and 4) (positive) and − 0.03 (variables 41 and 42) (negative).The range of correlations from near-zero to strong indicates meaningful variability suitable for downstream multivariate analyses, while also flagging potential multicollinearity concerns (r > 0.70). Overall, the correlation structure supports the existence of multiple latent domains and justifies dimensionality reduction via factor analysis [35].

The Appendix B provides a way to categorize participants based on their unique profiles across several p-factors. Some participants are more strongly associated with one particular factor (indicated by “X”), while others are more evenly distributed across multiple factors. The variance explained shows which factors contribute the most to understanding the overall variability in the data, with p-Factor 1 being the most influential.

Factor extraction and solution

A total of 58 Q-sorts were analyzed using PCA with Varimax rotation in PQMethod 2.35. The analysis produced an eight-factor solution that explained 66% of the total variance (Table 2). This level of explained variance is typical for Q-methodology studies and reflects the diversity of subjective viewpoints within the participant group.

Factors 1–3 emerged as the most prominent dimensions, each demonstrating strong eigenvalues, higher proportions of explained variance, and a greater number of significant scores. These factors represent the most dominant shared perspectives among participants. Factors 4–8 accounted for smaller but meaningful portions of variance and contributed additional nuance to the overall configuration of viewpoints. The distribution of eigenvalues and Humphrey’s Rule supports the conceptual coherence of the eight-factor solution.

Overview of factor structure

By-person factor analysis of the Q-sorts yielded an eight-factor solution representing distinct diagnostic reasoning profiles. Because some factors are represented by only n = 2 or n = 3 participants, the descriptions below should be interpreted tentatively as emerging psychological profiles rather than universally distributed categories [24, 25]. Factor arrays were constructed using only participants with statistically significant factor loadings (p < 0.01). Each factor was interpreted through systematic examination of its idealized Q-sort (factor array), distinguishing statements (p < 0.01), and consensus statements shared across factors (Tables 3 and 4). This approach is consistent with established Q-methodological standards for factor interpretation.

Factor 1: “pressure & authority-driven reasoner”

This profile was represented by the Q-sorts of three residents (aged 27–32). This factor describes diagnostic reasoning shaped by environmental pressure and authority influence, leading clinicians to commit early to an initial diagnosis and inadequately reassess it.

The factor array showed strong positive (+ 5) loadings for early diagnostic commitment under ward pressure, selective emphasis on information supporting the initial diagnosis, and reliance on initial patient data. Moderate positive rankings (+ 4) indicated limited additional investigations and acceptance of prior diagnoses. In contrast, negatively ranked statements (–5/–4) reflected resistance to disregarding dissenting viewpoints and rejection of fully autonomous decision-making without consultation (Table 3).

The distinguishing item” highlighting data in favor of the initial diagnosis in patient presentation” represents selective information framing under pressure and authority influence (Table 4).

Factor 2: “self-oriented reasoner”

This factor was represented by the Q-sorts of eight-participants (interns, residents, fellows; 24–39 years). This factor reflects a diagnostic reasoning pattern characterized by strong self-confidence and defensive clinical behavior, in which clinicians rely heavily on personal knowledge, experience, and perceived self-efficacy in decision-making.

High positive rankings (+ 5) were assigned to beliefs in superior diagnostic ability, confidence in self-assessment, and performing diagnostic or therapeutic measures to avoid legal consequences. Moderate positive rankings (+ 4) reflected fear of being labeled indifferent and reliance on prior experience, while neglect of contradictory evidence was strongly rejected (–5/–4) (Table 3).

The distinguishing statement “Believing in being more capable of diagnosing illness than other physicians” (+ 5) clearly defined this factor by emphasizing heightened self-efficacy and protective intent(Table 4).

Factor 3: “prototype-driven matcher”

This factor was represented by the Q-sorts of six interns and residents (26–38 years). This factor represents a diagnostic reasoning pattern dominated by prototype-based pattern recognition and social framing, in which clinicians rely on typical symptom patterns, age-related epidemiological expectations, and others’ portrayals of the patient.

Strong positive rankings (+ 5) emphasized age prevalence, stereotypical symptom patterns, and reliance on team members’ presentations. Additional high rankings (+ 4) reflected early diagnostic confirmation driven by intolerance of ambiguity and conformity to disease prototypes, with atypical findings downplayed. Negative rankings (–5/–4) indicated rejection of engaging with contradictory evidence or further investigations(Table 3).

The distinguishing statements highlighted reliance on disease stereotypes and early diagnostic closure under ambiguity, clearly differentiating this factor from the others(Table 4).

Factor 4: “fear- and pressure-anchored ignoring”

This profile, tentatively represented by the Q-sorts of two experienced fellows, reflects a potential diagnostic reasoning pattern marked by strong anchoring on the initial diagnosis, reinforced by emotional stress and contextual pressure, leading to selective ignoring of conflicting information.

The factor array (Table 3) showed high positive rankings (+ 5) for ignoring dissenting opinions and contradictory clinical findings. Additional high rankings (+ 4) reflected overreliance on first impressions, neglect of pre-test probability, limited attention to age and sex, and early diagnostic closure under crowded conditions and companion pressure, reinforced by fear of losing the patient.

Distinguishing statements (Table 4) emphasized ignoring opposing views, contradictory findings, and pre-test probability, along with stopping information search once a diagnosis felt satisfactory, clearly differentiating this factor.

Factor 5: “availability- and experience-based reasoner”

This factor was represented by the Q-sorts of two male interns (25–26 years). This factor represents a diagnostic reasoning profile driven by availability and experiential heuristics, where judgments rely on easily recalled diagnoses and prior lasting experiences rather than analytic evidence.

The factor array (Table 4) showed strong positive rankings (+ 5) for memory-retrieved and experience-based diagnoses and ignoring test sensitivity and specificity. Additional rankings (+ 4) reflected limited attention to age, sex, and disease prevalence, and preference for approaches accepted by colleagues. Negative rankings (–5/–4) indicated rejection of unquestioned reliance on others’ diagnoses and premature closure.

Distinguishing statements (Table 4) highlighted ignoring disease prevalence and sensitivity to others’ framing of patient deterioration, differentiating this reasoning profile.

Factor 6: “premature closure–satisficing reasoner”

This factor was represented by the Q-sorts of four interns and residents (25–28 years). This factor reflects a diagnostic reasoning profile characterized by premature closure and satisficing, in which clinicians accept an early diagnosis once it appears sufficient under contextual pressure.

The factor array (Table 4) showed strong positive rankings (+ 5) for early diagnosis driven by crowded environments, companion pressure, reliance on recent cases, and ignoring further evidence after reaching a satisfactory diagnosis. Additional rankings (+ 4) reflected stopping information search and accepting initial diagnoses without sufficient data. Negative rankings (–5/–4) rejected ambiguity-driven confirmation and social framing.

The distinguishing statement (Table 4) was performing diagnostic or therapeutic measures due to fear of losing the patient, highlighting fear-driven action as the defining feature.

Factor 7: “diagnostic momentum with selective cue rejection“

This factor was represented by the Q-sorts of two interns and one resident (30–31 years). Factor 7 reflects a diagnostic reasoning profile characterized by diagnostic momentum reinforced by social input and fear of patient loss, in which early diagnostic direction is sustained through reliance on others’ judgments and recent clinical experiences.

The factor array (Table 3) showed strong positive rankings (+ 5) for performing diagnostic or therapeutic actions due to fear of losing the patient, focusing on previous diagnoses suggested by senior clinicians, and decision-making influenced by recent cases. Additional high rankings (+ 4) reflected reliance on how other team members portrayed the patient’s condition, overreliance on the first information received, and ignoring alternative diagnoses due to an existing diagnostic label. In contrast, negatively ranked statements (–4) indicated rejection of demographic-based reasoning, fashion-driven practice, survival-rate–focused decision-making, and sociocultural framing detached from disease mechanisms.

The distinguishing statement for this factor highlighted ignoring the underlying cause of disease while focusing on patients’ and companions’ personality and appearance characteristics, marking selective rejection of etiological cues as a defining feature of this reasoning profile (Table 4).

Factor 8: “anchoring-driven diagnostic rigidity”

This factor was represented by the Q-sorts of two experienced trainees (43–44 years). Factor 8 reflects a diagnostic reasoning profile characterized by strong commitment to the initial diagnosis and marked resistance to diagnostic revision, suggesting rigidity in the presence of cognitive demands.

The factor array (Table 3) showed strong positive rankings (+ 5) for ignoring alternative diagnoses, high confidence in the correctness of the initial diagnosis, and difficulty in changing the initial diagnostic impression. Additional high rankings (+ 4) reflected overemphasis on the first possible diagnosis, overreliance on initial patient information, ignoring new information that contradicted the first diagnosis, and acceptance of an early diagnosis before sufficient data collection. In contrast, negatively ranked statements (–5/–4) indicated rejection of actively searching for disconfirming evidence, reliance on easily retrievable diagnoses, disregard for test sensitivity and specificity, overemphasis on patient personality characteristics, unquestioned acceptance of diagnoses in the file, and neglect of pre-test probability.

Distinguishing statements for this factor highlighted ignoring alternative diagnoses and contradictory information, alongside sensitivity to previous diagnoses suggested by senior clinicians and the influence of recent diagnostic experiences, differentiating this rigid reasoning pattern from other factors (Table 4).

A consensus statement was observed across Factors 5, 7, and 8, indicating a shared tendency to diagnose patients under the influence of previous diagnostic errors. Although this statement did not distinguish between these factors, it highlights a common experiential influence on diagnostic reasoning, suggesting that prior errors shaped subsequent diagnostic judgments across multiple reasoning profiles.

Discussion

This study offers an interpretive, person-centred contribution to the understanding of diagnostic reasoning by identifying eight distinct cognitive profiles using Q-methodology. Rather than framing diagnostic error as a unitary construct, the findings suggest that diagnostic vulnerability emerges from heterogeneous configurations of cognitive biases, emotional influences, social interactions, and organizational pressures. This perspective aligns with contemporary views that emphasize diagnostic reasoning as a situated and context-sensitive process and challenges reductionist approaches that attribute error primarily to individual cognitive failure. As such, the findings question the effectiveness of uniform diagnostic improvement strategies and instead point toward the value of tailored educational and system-level interventions [37–40].

Across several profiles, early diagnostic commitment emerged as a recurrent pattern shaping vulnerability. Factors 1, 4, 6, and 8 were characterised by premature fixation on an initial diagnosis, often under conditions of time pressure, emotional strain, or hierarchical influence. These patterns are consistent with well-documented cognitive biases such as anchoring, premature closure, and confirmation bias [41, 42]. However, the present findings extend existing literature by illustrating how these biases are embedded within social and organisational contexts rather than operating solely as individual cognitive tendencies. Crowded clinical environments, concern about patient deterioration, and deference to senior clinicians’ diagnostic opinions appeared to amplify early commitment. For example, Factor 1 (“Reasoner under pressure and authority”) highlights how hierarchical dynamics may reinforce anchoring and diagnostic momentum, supporting evidence that unchallenged prior diagnoses can propagate within teams [41].

Emotional influences were prominent across multiple diagnostic profiles and appeared to shape both information processing and decision thresholds. Fear, anxiety, and concern about professional judgement were particularly salient in high-risk and uncertain clinical situations. Factors 4 and 6 illustrate how fear of losing the patient and environmental pressure, may lead clinicians to disregard disconfirming evidence and prematurely conclude diagnostic reasoning. These findings resonate with prior research demonstrating that emotional states both negative and positive play a significant role in clinical decision-making and patient safety. Negative emotions may narrow attentional focus and reduce cognitive flexibility, while positive emotional engagement has been associated with improved communication and care quality [43, 44]. From an educational perspective, these findings underscore the importance of recognising emotional regulation as an integral component of diagnostic competence.

The profile2(Self-Oriented) Reasoner also draw attention to the role of psychological safety in shaping diagnostic reasoning. When clinicians anticipate blame, criticism, or negative evaluation, decisiveness and self-protective reasoning may be prioritised over open diagnostic exploration. This interpretive lens helps explain why some participants demonstrated defensive reasoning patterns despite explicitly rejecting the notion that they ignored alternative diagnoses [45]. In such contexts, maintaining professional credibility may take precedence over reflective reasoning, reinforcing rigid diagnostic pathways and limiting opportunities for adaptive learning. These observations align with broader educational literature highlighting psychological safety as a prerequisite for learning from uncertainty and error.

A central contribution of this study lies in its clarification of how diagnostic vulnerability varies across stages of professional development. Factor 5 (Availability- and Experience-Based Reasoner), represented exclusively by young interns (aged 25–26), illustrates how limited clinical exposure promotes dependence on easily recalled or recent diagnoses, increasing susceptibility to availability bias [46]. Similarly, Factor 3 (“Prototype-Driven Matcher”), prevalent among interns and junior residents, reflects reliance on disease prototypes and pattern recognition. While such strategies are characteristic of developing expertise and can enhance efficiency, they may increase vulnerability to framing effects and premature closure in atypical presentations [47, 48].

As clinicians progress into residency, diagnostic vulnerability appears to shift from knowledge-related limitations toward the influence of systemic and environmental pressures. Residents in the 27–32 age range were strongly represented in Factor 1 (Pressure & Authority-Driven Reasoner) and Factor 6 (Premature Closure–Satisficing Reasoner), suggesting heightened sensitivity to workload, time constraints, and hierarchical dynamics. Increasing responsibility during this stage may encourage satisficing accepting a diagnosis that is sufficient rather than optimal under pressure thereby disrupting analytical reasoning processes. These findings suggest that residency represents a critical transitional period in which cognitive load and organisational demands exert a substantial influence on diagnostic reasoning and merit particular educational attention.In contrast, advanced experience was associated with different forms of diagnostic vulnerability. Factor 4 (Fear- and Pressure-Anchored Ignoring) and Factor 8 (Anchoring-Driven Diagnostic Rigidity) were represented primarily by older, experienced fellows (aged 43–48) and were characterised by diagnostic rigidity and selective disregard of disconfirming evidence. These profiles suggest that extensive experience, while valuable for efficiency and confidence, may also foster entrenched reasoning patterns and overconfidence, reducing adaptability when confronted with conflicting data. Collectively, these findings challenge the assumption that increasing experience uniformly protects against diagnostic error and instead support the view that expertise reshapes the nature of error risk from knowledge-based gaps toward bias-driven rigidity.

The diversity of diagnostic reasoning profiles identified in this study carries important implications for medical education. Educational interventions that focus solely on increasing factual knowledge or raising awareness of cognitive biases are unlikely to be sufficient. Consistent with educational theory, the findings support early and sustained emphasis on metacognitive awareness, enabling learners to recognise their own reasoning tendencies, emotional responses, and contextual vulnerabilities [42]. Evidence suggests that debiasing approaches are more effective when they combine metacognitive training with deliberate slowing of decision-making and explicit encouragement of alternative hypothesis generation [49].

Importantly, educational strategies should be developmentally informed. For early-career clinicians, curricula should prioritise structured knowledge development, guided exposure to atypical cases, and explicit discussion of the limitations of prototype-based reasoning. For residents, educational interventions should address cognitive load management, resistance to satisficing under pressure, and navigation of hierarchical influences through strategies such as diagnostic pause points and supervised reflective dialogue. For more experienced clinicians, ongoing professional development should emphasise maintaining cognitive flexibility through structured reflection, feedback on diagnostic outcomes, and engagement with cases that highlight missed or delayed diagnoses.

Beyond individual-level education, the findings underscore the influence of organisational and cultural factors on diagnostic safety. Promoting psychological safety within clinical teams is essential to support open discussion of uncertainty and diagnostic error without fear of judgement or blame [50]. System-level interventions including team-based diagnostic reviews, protected time for reflection, and institutional support for learning from error may complement individual educational efforts. Addressing emotional and defensive drivers of reasoning, such as fear of litigation and reputational harm, is also necessary to foster a learning-oriented culture that prioritises improvement over fault-finding.

A central contribution of this study lies in its clarification of how individual decision-maker characteristics and subjective vulnerabilities vary. While we must be cautious and highly tentative in comparing factors that only represent a small number of participants (e.g., n = 2 or n = 3), Q-methodology allows us to see these small clusters as distinct, logically coherent viewpoints that exist within the clinical environment [24, 25].

Strengths and limitations

This study has several notable strengths. First, the active involvement of key stakeholders throughout the research process enhanced the relevance and credibility of the findings. Stakeholder contributions were instrumental in shaping the initial focus, identifying clinically meaningful issues, and refining theoretical interpretations, thereby strengthening the practical applicability of the results. Second, the use of Q-methodology represents a methodological strength, as it enabled a systematic, person-centred exploration of diagnostic reasoning. Rather than aiming for statistical generalisability, this approach captured shared subjective viewpoints and revealed distinct diagnostic reasoning profiles that may remain obscured using conventional quantitative methods. In addition, the realist-informed approach to literature synthesis supported the development of nuanced, theory-driven interpretations and provided a coherent framework for understanding how cognitive and contextual factors interact in diagnostic reasoning.

Several limitations should also be acknowledged. The study was conducted within a single paediatric referral hospital, which may limit the transferability of the findings to other clinical settings, specialties, or healthcare systems. Diagnostic reasoning is highly context-dependent, and the identified profiles may manifest differently in other environments. Furthermore, the cross-sectional design precludes examination of how diagnostic reasoning patterns evolve over time or in response to educational or organisational interventions. Future research should therefore incorporate multi-institutional and multi-specialty samples and employ longitudinal designs to explore developmental changes in diagnostic reasoning and to evaluate the impact of targeted educational strategies.

Conclusions

This study demonstrates that diagnostic reasoning errors arise from diverse and interacting cognitive, emotional, and contextual influences rather than from isolated deficits in knowledge. By identifying eight distinct diagnostic reasoning profiles, the findings highlight the heterogeneity of clinicians’ reasoning and underscore the need for developmentally informed and context-sensitive educational approaches. Recognising and addressing these differences may support more effective diagnostic education and contribute to safer diagnostic practice.

These profiles also open the door to future investigations into how foundational psychological traits influence CDM. For instance, attachment styles may heavily influence profiles such as the “Self-Oriented Reasoner” or the “Pressure & Authority-Driven Reasoner.” A clinician with an insecure or avoidant attachment style might react differently to hierarchical pressure or patient demands compared to one with a secure attachment style.Currently, the mapping of Q-sort error profiles to specific attachment styles remains an uninvestigated frontier in CDM. Future studies must account for this, while also controlling for the confounding effects of the patient’s and caregivers’ attachment styles on the clinician’s decision-making process.

Abbreviations

AHRQ

Agency for Healthcare Research and Quality

CVR

Content Validity Ratio

CVI

Content Validity Index

QTIP

Quality through Technology and Innovation in Pediatrics

PCA

Principal Component Analysis

SD

Standard Deviation

Appendix A

Table 5.

X’ indicating a defining patient

Correlation matrix between sorts
	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48
1	1
2	0.41	1
3	0.22	0.32	1
4	0.11	-0.08	-0.18	1
5	0.06	0.37	0.34	-0.71	1
6	0.64	0.44	0.07	0.21	0.1	1
7	0.27	0.09	0.19	-0.14	0.26	0.15	1
8	0.2	0.42	0.1	0.41	-0.08	0.33	0.33	1
9	0.36	0.31	0.38	0.07	0.17	0.32	0.22	0.27	1
10	0.27	0.49	0.22	-0.07	0.34	0.23	0.18	0.29	0.51	1
11	0.04	0.31	-0.03	0.24	-0.16	0.22	-0.25	0.32	0.01	0.14	1
12	0.24	0.05	-0.04	0.51	-0.41	0.26	0.22	0.28	0.16	-0.21	0.1	1
13	0.15	0.17	0.1	0.02	0.1	0.12	0.09	0.34	0.34	0.37	0.19	-0.16	1
14	-0.07	0.3	0.5	-0.1	0.22	-0.11	0.22	0.23	0.07	0.11	0.1	-0.07	-0.01	1
15	0.36	0.18	0.28	-0.34	0.4	0.22	0.24	-0.13	0.39	0.26	0.04	0.11	0.03	0.08	1
16	0.16	0.33	0.54	-0.35	0.56	0.28	0.13	0.11	0.4	0.42	-0.04	-0.23	0.28	0.2	0.25	1
17	0.09	0.41	0.12	0.26	-0.02	0.11	0.12	0.66	0.33	0.2	0.24	0.13	0.26	0.34	-0.16	0.1	1
18	0.06	0.27	0.56	-0.43	0.7	0.04	0.19	-0.05	0.31	0.41	-0.13	-0.34	0.06	0.28	0.46	0.45	0	1
19	0.24	0.25	0.42	-0.62	0.77	0.09	0.36	-0.14	0.36	0.42	-0.27	-0.3	0.06	0.12	0.58	0.52	-0.02	0.77	1
20	-0.12	0.16	-0.09	0.34	-0.32	-0.03	-0.24	0.31	-0.22	0.02	0.24	0.11	-0.12	0.02	-0.4	-0.21	0.1	-0.33	-0.52	1
21	0.09	0.41	0.12	0.26	-0.02	0.11	0.12	0.66	0.33	0.2	0.24	0.13	0.26	0.34	-0.16	0.1	1	0	-0.02	0.1	1
22	0.62	0.35	0.13	0.2	0.1	0.59	0.28	0.22	0.36	0.24	0.03	0.18	0	0.04	0.16	0.16	0.28	0.15	0.29	-0.16	0.28	1
23	-0.33	0.01	0.06	-0.24	0.15	-0.27	-0.11	-0.16	-0.13	-0.07	-0.05	-0.11	-0.08	0.35	-0.14	0.1	0.12	0	-0.01	0.06	0.12	-0.14	1
24	-0.57	-0.05	-0.05	0.07	-0.12	-0.45	-0.07	0.24	-0.3	-0.14	0.1	-0.04	0.15	0.4	-0.37	-0.13	0.31	-0.2	-0.37	0.35	0.31	-0.49	0.43	1
25	0.18	0.18	0.15	0.41	-0.19	0.27	0.3	0.52	0.29	0.2	0.18	0.23	-0.01	0.22	0.04	0.03	0.34	0.13	0	-0.01	0.34	0.37	-0.14	-0.19	1
26	0.23	0.52	0.07	0.01	0.33	0.24	0.02	0.44	0.19	0.37	0.51	-0.03	0.26	0.17	0.2	0.12	0.51	0.14	0.14	0.08	0.51	0.11	-0.07	0.12	-0.02	1
27	-0.06	0.34	0.29	0.04	0.1	0.08	0.15	0.35	0.17	0.19	0.33	0.04	0.27	0.39	-0.12	0.26	0.42	-0.04	-0.01	0.03	0.42	0.07	0.32	0.36	0.12	0.33	1
28	0.57	0.45	0.38	0.24	-0.01	0.5	0.23	0.3	0.33	0.24	0.24	0.39	0.04	0.21	0.46	0.12	0.22	0.14	0.18	-0.03	0.22	0.43	-0.3	-0.25	0.33	0.3	0.12	1
29	-0.31	-0.29	-0.23	-0.28	0.03	-0.37	0.02	-0.43	-0.2	-0.23	-0.44	-0.12	-0.31	-0.09	-0.19	-0.19	-0.22	0.02	0.08	-0.17	-0.22	-0.14	0.27	0.1	-0.31	-0.4	-0.21	-0.44	1
30	0.28	0.25	0.27	0.28	-0.1	0.35	0.07	0.3	0.57	0.28	0.1	0.24	0.31	0.3	0.14	0.36	0.45	0.03	0.05	-0.1	0.45	0.4	0.1	0	0.14	0.19	0.31	0.36	-0.22	1
31	0.17	0.23	0.28	-0.02	0.05	0.07	0.05	0.16	0.27	0.3	0.18	0.06	0.12	0.2	0.23	0.16	0.18	0.13	0.13	-0.14	0.18	0.04	0.07	-0.02	0.15	0.18	0.22	0.46	-0.28	0.23	1
32	-0.06	0.04	0.2	-0.24	0.07	-0.06	-0.03	-0.15	-0.03	-0.08	0.01	-0.3	0.03	0.27	-0.06	0.01	-0.13	0.1	0.02	0	-0.13	-0.13	0.13	0.08	-0.11	-0.09	0.14	-0.12	0.27	0.02	-0.18	1
33	0.08	0.07	0.01	0.32	-0.23	0.18	0.07	0.31	-0.18	-0.24	0.13	0.52	-0.34	0.17	-0.08	-0.22	0.25	-0.26	-0.2	0.31	0.25	0.23	0.04	0.15	0.1	0.24	0.11	0.34	-0.14	0.18	0.04	-0.29	1
34	0.14	0.32	0.6	-0.11	0.18	0.04	0.13	0.21	0.18	0.2	0.01	0.02	0.03	0.35	0.06	0.43	0.18	0.34	0.28	0.13	0.18	0.15	0.1	-0.08	0.14	0.05	0.22	0.29	-0.25	0.31	0.14	-0.1	0.27	1
35	-0.04	0.22	0.08	-0.04	0.04	-0.16	-0.27	0.24	-0.1	0.1	0.21	-0.21	0.25	-0.01	-0.25	0.03	0.19	-0.04	-0.1	0.35	0.19	-0.16	0.11	0.29	-0.11	0.27	0.24	-0.13	-0.34	-0.03	-0.06	-0.13	0.01	0.2	1
36	-0.06	0.27	0.11	-0.3	0.31	-0.03	-0.03	-0.01	0.13	0.21	0.06	-0.17	-0.06	0.28	0.12	0.2	0.16	0.11	0.16	0.01	0.16	-0.08	0.29	0.09	0.02	0.32	0.5	0.04	-0.12	0.12	0.12	0.13	0.07	0.23	0.25	1
37	-0.05	0.24	0.21	-0.34	0.26	-0.15	0.09	-0.01	-0.12	-0.11	0.1	-0.18	-0.07	0.3	-0.09	0.07	0.04	0.04	0.03	-0.07	0.04	-0.21	0.32	0.23	-0.1	0.15	0.28	-0.19	0.16	-0.18	0.05	0.47	-0.13	-0.1	0.04	0.31	1
38	0.28	0.53	0.5	-0.27	0.56	0.32	0.27	0.28	0.38	0.48	0.2	-0.16	0.15	0.38	0.28	0.49	0.22	0.45	0.53	-0.14	0.22	0.28	0.22	-0.11	0.22	0.4	0.51	0.28	-0.3	0.14	0.28	0.09	0.01	0.34	0.12	0.35	0.28	1
39	-0.3	-0.09	0.17	0.02	0.14	-0.27	0.08	0.13	0.1	0.11	-0.21	-0.08	0.11	0.25	-0.16	0.2	0.26	0.1	0.14	-0.01	0.26	-0.26	0.14	0.47	-0.07	0.14	0.27	-0.02	-0.02	0.15	0.2	-0.11	0.11	0.18	0.11	0.19	-0.23	0.09	1
40	-0.18	0.09	0	0.09	-0.09	-0.24	0.1	0.22	0.06	-0.26	-0.1	0.17	0	0.19	-0.31	-0.06	0.42	-0.17	-0.07	0.02	0.42	0.04	0.2	0.28	0.19	0.02	0.31	-0.17	0.22	0.1	-0.2	0.09	0.15	-0.06	0.06	0.06	0.3	0.05	0.05	1
41	-0.14	-0.04	-0.18	-0.01	0.05	-0.08	-0.2	-0.11	0.05	0.26	0.13	-0.18	0.33	-0.16	0	0.06	0.04	0.13	0.01	-0.07	0.04	-0.06	0.27	0.1	-0.13	0.14	0	-0.21	-0.04	0.08	0.07	-0.28	-0.12	0.02	0.07	0.08	-0.2	-0.02	0.12	-0.25	1
42	0.21	0.07	0.47	-0.3	0.43	0.09	0.25	-0.13	0.4	0.16	-0.15	0.01	-0.13	0.16	0.61	0.3	-0.01	0.63	0.68	-0.53	-0.01	0.17	-0.07	-0.24	0.15	0.08	0.02	0.3	0.12	0.11	0.21	0.03	-0.04	0.17	-0.26	0.14	0.02	0.32	0.15	-0.1	-0.06	1
43	0.32	0.23	0.07	-0.22	0.19	0.31	0.2	-0.02	0.31	0.17	-0.07	-0.13	0.03	-0.01	0.23	0	0.09	0.26	0.35	-0.43	0.09	0.29	-0.11	-0.4	0.22	0	0.03	0.08	0.08	0.04	0.1	0.35	-0.33	-0.18	-0.12	0.06	0.29	0.29	-0.34	-0.01	-0.18	0.35	1
44	0.16	0.31	0.06	0.06	-0.02	0.21	0.27	0.27	0.1	0	0.27	0.17	-0.26	0.41	0.02	-0.15	0.25	-0.09	-0.04	0.03	0.25	0.29	0.07	-0.09	0.3	0.16	0.26	0.3	0	0.2	0.32	0.2	0.33	0.11	-0.18	0.16	0.28	0.28	-0.15	0.02	-0.3	0.06	0.36	1
45	0.06	0.21	-0.14	0.45	-0.37	0.23	-0.03	0.36	0.05	0.17	0.43	0.19	0.17	0.28	-0.2	-0.13	0.34	-0.27	-0.45	0.12	0.34	0.04	0.1	0.21	0.39	0.16	0.37	0.22	-0.14	0.31	0.31	0.12	0.04	-0.11	-0.13	0.09	0.12	0.03	-0.04	0.05	0.01	-0.2	0.18	0.41	1
46	0.16	0.08	0.24	0.32	-0.3	0.23	0.11	0.34	0.37	-0.09	0.13	0.45	0.11	0.16	0.19	-0.02	0.16	-0.22	-0.25	0.11	0.16	0.04	-0.23	0.13	0.2	0.09	0.24	0.49	-0.24	0.39	0.28	0.04	0.36	0.06	-0.07	0.09	-0.01	0	0.1	0.07	-0.29	0.15	-0.01	0.33	0.27	1
47	0.13	0.3	0.45	-0.3	0.52	0.01	0.42	0.15	0.32	0.45	-0.13	-0.15	0.19	0.4	0.36	0.45	0.18	0.48	0.54	-0.22	0.18	0.14	0.03	0	0.06	0.22	0.24	0.23	-0.26	0.16	0.26	-0.1	-0.06	0.4	0.12	0.26	0.04	0.49	0.3	-0.05	0	0.29	0.16	0.01	-0.09	-0.05	1
48	0.18	0.47	0.3	0.1	0.25	0.35	0.09	0.43	0.42	0.56	0.33	0.09	0.17	0.23	0.29	0.47	0.2	0.35	0.18	0.19	0.2	0.18	-0.02	-0.07	0.36	0.32	0.2	0.23	-0.45	0.27	0.14	-0.19	-0.01	0.31	0.16	0.14	-0.18	0.46	0.03	-0.16	0.07	0.17	-0.02	-0.01	0.12	0.04	0.37	1

Appendix B

Table 6.

Person factor matrix with an X indicating patients who define the p-factor

	Person factor Score
Sort ( Participant )	p-Factor 1		p-Factor 2		p-Factor 3		p-Factor 4		p-Factor 5		p-Factor 6		p-Factor 7		p-Factor 8
1	0.11		0.18		0.76	X	-0.02		-0.01		0.06		0.16		-0.057
2	0.48		0.38		0.35		0.20		0.02		0.31		0.02		0.23
3	-0.11		0.77	X	0.00		0.22		-0.01		0.01		0.36		0.12
4	-0.12		-0.51		0.20		-0.27		0.03		0.30		0.47		0.29
5	0.26		0.73	X	0.00		0.09		0.03		-0.04		-0.42		-0.21
6	0.21		0.07		0.75	X	-0.01		0.05		0.1		0.20		0.04
7	-0.17		0.34		0.20		0.06		-0.16		0.34		0.09		-0.29
8	0.18		0.06		0.21		-0.05		0.07		0.67	X	0.27		0.36
9	-0.00		0.39		0.30		-0.07		0.4		0.28		0.36		-0.24
10	0.34		0.43		0.23		-0.11		0.5		0.11		0.04		0.03
11	0.63	X	-0.2		0.14		0.10		0.12		0.01		0.23		0.34
12	-0.08		-0.23		0.24		-0.25		-0.4		0.22		0.49		-0.01
13	0.1		0.1		0.00		-0.08		0.74	X	0.21		0.1		0.10
14	0.14		0.42		-0.31		0.37		-0.16		0.29		0.33		0.056
15	0.23		0.46		0.30		-0.13		-0.03		-0.32		0.20		-0.42
16	-0.00		0.72	X	0.05		0.02		0.32		0.04		0.04		0.07
17	0.29		0.05		-0.04		-0.07		0.07		0.86	X	0.13		0.04
18	0.04		0.76	X	0.08		0.02		0.15		-0.08		-0.1		-0.24
19	0.04		0.78	X	0.17		-0.05		0.04		-0.02		-0.27		-0.45
20	0.06		-0.21		-0.07		-0.01		-0.17		0.05		0.05		0.77	X
21	0.29		0.05		-0.04		-0.07		0.07		0.86	X	0.13		0.04
22	0.02		0.16		0.67	X	-0.08		-0.1		0.38		0.033		-0.16
23	0.19		0.09		-0.55	X	0.22		-0.06		0.14		-0.12		-0.04
24	0.11		-0.12		-0.77	X	0.058		0.001		0.29		0.09		0.26
25	-0.07		0.05		0.37		0.07		0.03		0.42		0.34		0.03
26	0.75	X	0.17		0.09		-0.11		0.02		0.30		-0.04		0.10
27	0.36		0.21		-0.26		0.3		0.09		0.42		0.26		0.14
28	0.25		0.26		0.46		-0.13		-0.20		0.05		0.6		-0.04
29	-0.35		-0.18		-0.3		0.21		-0.16		-0.01		-0.33		-0.46
30	0.04		0.18		0.1		-0.08		0.24		0.4		0.53	X	-0.052
31	0.42		0.18		-0.05		-0.05		0.08		-0.05		0.51		-0.21
32	-0.13		0.03		-0.09		0.79	X	0.14		-0.11		0.02		-0.04
33	0.17		-0.07		0.01		-0.27		-0.73	X	0.26		0.28		0.19
34	-0.04		0.63	X	-0.01		-0.09		-0.16		0.1		0.21		0.35
35	0.23		0.12		-0.16		-0.056		0.12		0.11		-0.24		0.6	X
36	0.47	X	0.30		-0.25		0.21		-0.076		0.05		0.01		0.01
37	0.23		0.07		-0.18		0.75	X	-0.11		0.06		-0.16		-0.05
38	0.40		0.64	X	0.17		0.26		0.04		0.2		0.01		0.02
39	-0.02		0.29		-0.59	X	-0.34		0.06		0.23		0.19		-0.04
40	-0.24		-0.08		-0.20		0.24		-0.17		0.67	X	-0.12		-0.00
41	0.33		-0.06		-0.23		-0.44		0.42		-0.06		-0.16		-0.1
42	0.01		0.56		0.09		-0.03		-0.09		-0.08		0.17		-0.57
43	0.09		0.06		0.44		0.5		0.19		0.11		-0.09		-0.47
44	0.33		-0.06		0.19		0.45		-0.38		0.22		0.33		-0.16
45	0.33		-0.38		0.02		0.3		0.23		0.29		0.5		0.02
46	-0.00		-0.04		0.05		0.07		-0.13		0.09		0.78	X	0.02
47	0.12		0.71	X	-0.04		-0.06		0.09		0.15		0.04		-0.07
48	0.24		0.46		0.27		-0.14		0.24		0.11		0.18		0.35
% Explained Variance	19		13		10		7		6		5		4		4

Authors’ contributions

All authors have read and approved the manuscript. Conceptualization: AO; Methodology: AO, SZ, FJ; Formal analysis: AO, SZ, FJ; Data curation: AO, SZ;, Writing - original draft preparation: SZ; Writing - review and editing: AO.

Funding

This project was funded by Isfahan University of Medical Sciences. Iran. Grant No. 3401125.

Data availability

All data generated or analysed during this study are included in this published article.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee and the Scientific Committee of Isfahan University of Medical Sciences (Scientific Code: 3401125, Ethics Code: IR.ARI.MUI.REC.1401.080). During the study, the university’s ethical guidelines were adhered to in line with the Declaration of Helsinki. This included securing informed consent from all participants, guaranteeing the confidentiality of their data, and allowing them the right to withdraw from the research at any stage.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.