Comprehensive Study Notes: Profiling Non-Cognitive Traits, Stress, Burnout, and Engagement

Systematic Review of Non-Cognitive Traits in Health Professionals

• Theoretical Context & Rationale

  • Non-cognitive traits—comprising personality traits, behaviour styles, and emotional intelligence ($\text{EI}$)—are theorised to predict professional characteristics, career choice, and clinical outcomes across health disciplines.

  • Practitioners from specific health professions may represent homogenous cohorts in terms of non-cognitive trait profiles due to occupational requirements, routines, rewards, and career selection processes.

  • Personality is conceptualised as enduring patterns of feelings, thoughts, and behaviours exhibited across environments. It is stable over time, influences conflict management and professional perspectives, and precedes specialty selection.

  • Behaviour styles develop from temperament and reflect coordinated actions or inactions in response to internal and external stimuli. They are amenable to modification through cognitive reasoning and reflection over time and across contexts.

  • Emotional intelligence ($\text{EI}$) represents non-cognitive capabilities (e.g., empathy, integrity, professionalism) that govern environmental coping, self-awareness, interpersonal conflict reduction, and patient-centred care quality across five core domains: self-regulation, self-awareness, empathy, motivation, and social skills.

  • Health professionals face high occupational exposure to physical and psychological fatigue, rendering them susceptible to burnout—a syndrome characterised by high emotional exhaustion, high depersonalisation, and low personal accomplishment. Higher $\text{EI}$ and emotion regulation facilitate problem-focused coping, mitigating burnout vulnerability.

Methodological Framework and Screening Protocol (PRISMA & MMAT)

• Protocol Registration & Standards

  • The systematic review protocol was constructed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols ($\text{PRISMA-P}$) and registered on PROSPERO under registration number $\text{CRD42020155113}$.

• Database Search Strategy

  • A two-concept search strategy was developed: Concept 1 covered non-cognitive traits (personality, behaviour styles, $\text{EI}$ assessment tools); Concept 2 covered health professionals (medicine, nursing, dentistry, allied health professions such as physiotherapy, occupational therapy, speech pathology).

  • Four electronic databases were systematically searched from $\text{1980}$ onwards: CINAHL, PubMed, EMBASE (via OVID), and ProQuest Central (accessing $\text{47}$ constituent databases). Polyglot Search Translator was used to convert search strings across platforms.

  • Exclusion filter: individuals under $\text{18}$ years of age.

• Study Selection Flow & Quantitative Metrics

  • Total records identified across databases: CINAHL ($n = 3870$), PubMed ($n = 8903$), EMBASE ($n = 6159$), ProQuest Central ($n = 1927$), manually added ($n = 215$).

  • Duplicates removed before screening: $n = 6791$.

  • Total records screened by title/abstract: $n = 14,283$; records excluded: $n = 13,574$.

  • Reports sought for retrieval: $n = 709$; reports not retrieved due to missing full text: n = 32$.\n - Full-text reports assessed for eligibility: n = 677$; reports excluded with reasons ($n = 356):\n - Incorrect study design: n = 135\n - Data not available for extraction: n = 108\n - Unreliable, invalid, or incorrect assessment tool: n = 24\n - Study population involving non-health professionals: n = 19\n - Article not in English: n = 39\n - Not a full paper (abstracts, conference proceedings): n = 31\n - Total studies included in final synthesis: n = 321$($n = 292$in quantitative meta-aggregation;$n = 35$in qualitative narrative synthesis;$n = 6 included in both).\n\n\n\n- **Quality Appraisal & Synthesis Software**\n - Screened using Covidence online systematic review platform.\n - Methodological quality assessed via the Mixed Methods Appraisal Tool (\text{MMAT}$, 2018 version), using a dichotomous score converted to a percentage ($\ge 75\% designated as high quality).\n - Inter-rater agreement for initial search: 74.05\%$(Cohen's Kappa$\kappa = 0.841$,$p < 0.01$); updated search:$88.89\%$(Cohen's Kappa$\kappa = 0.602$,$p < 0.05$);$\text{100\%} consensus achieved after third-reviewer consultation.\n - High quality (\ge 75\%$threshold):$217$studies; lower quality ($< 75\%$threshold):$104 studies.\n - Quantitative meta-aggregation performed using Exploratory Software for Confidence Intervals (\text{ESCI}$) Meta-Analysis software. Missing standard deviations ($\text{SD}$) were imputed via Cochrane calculator from$p$-values,$\text{95\%}$confidence intervals, or imputed from maximum$\text{SD}$of matching tool subscales ($n = 31$studies); sample mean and$\text{SD}$estimated from median and interquartile range ($\text{IQR}$) for$n = 2 studies.\n\n# Quantitative and Narrative Profiling Across Health Professions\n\n- **Demographic Scope of Synthesised Literature**\n - Combined sample size: \text{112,691}$health practitioners across$\text{53}$countries, ranging from$10$to$5148 participants per study.\n - Profession breakdown:\n - Nurses: n = 64,250\n - Doctors / Medical Practitioners: n = 36,029\n - Allied Health Professionals: n = 5068$(Occupational Therapists$n = 1944$, Dietitians$n = 776$, Physiotherapists$n = 684$, Pharmacists$n = 298$, Psychologists$n = 118$, Radiologists$n = 117$, Social Workers$n = 8$, Non-defined Allied Health$n = 1123)\n - Dentists: n = 4139\n - Unidentified Health Professionals: n = 2247\n - Paramedics: n = 744\n - Nursing Assistants: n = 177\n - Pathologists: n = 37\n - Total unique measurement tools evaluated: \text{148}$tools across all constructs ($84$personality tools with$281$subscales;$8$behaviour tools across$7$categories;$56$$\text{EI}$tools with$102 subscale items).\n\n- **Personality Trait Profiling Across Disciplines**\n - Personality evaluated in 171$studies ($n = 65,581$participants;$143$meta-aggregated,$20 narratively synthesised).\n - *Cross-Professional Commonalities*: All health disciplines exhibit high agreeableness, cooperativeness, and self-directedness alongside low neuroticism, supporting high emotional stability and team functionality.\n - *Cognitive Temperament (Myers-Briggs Type Indicator / Temperament Frameworks)*:\n - Most health professions show prominent Sensing-Judging (\text{SJ}) temperaments (e.g., ISTJ, ESTJ), preferring factual, structured, objective, and decisive processing.\n - *Exception*: Occupational Therapists display dominant Sensing-Perceiving (\text{SP}$,$49\%$) and Intuitive-Feeling ($\text{NF}$,$27\%) traits, prioritising holistic human function, psychological well-being, and adaptive experience over rigid structural impairment models.\n - *Nursing (98$studies,$n = 31,971$)*: High agreeableness, assertiveness, dominance, conscientiousness, hardiness, sensing, judging; low abstractedness, apprehension, boldness, imagination, independence, neuroticism. Moderate-to-severe psychological distress in nurses correlates with$53\%$negative personality traits versus$97\% positive traits in low-distress cohorts.\n - *Nursing Assistants (1$study,$n = 177$)*: High likeability ($18.63 ± 3.07$), ambition ($20.58 ± 5.28$); low sociability ($11.33 ± 4.76$), intellectance/openness ($11.96 ± 4.52).\n - *Medicine (52$studies,$n = 21,125)*: High dominance, instrumentality, perfectionism, reasoning, reward-dependence, sensitivity, shrewdness, anxiety, agreeableness, openness, tension; low narcissism, abstractedness, neuroticism, psychoticism, social boldness.\n - *Dentistry (7$studies,$n = 3664$)*: Predominantly ISTJ ($16.0\text{–}54.0\%$) and ESTJ ($13.0\text{–}14.3\%); aspiration driven by intrinsic goals.\n - *Allied Health Collective (n = 1123$)*: High cooperativeness ($83.38 ± 0.00$), self-directedness ($77.39 ± 0.00$), persistence ($72.38 ± 0.00$), reward dependence ($71.87 ± 0.00$); low self-transcendence ($44.98 ± 0.00).\n - *Dietetics (3$studies,$n = 776$)*: High cooperativeness ($80.79 ± 2.01$), persistence ($73.53 ± 5.03$), self-directedness ($74.30 ± 6.96$), harm avoidance ($56.85 ± 9.78$), novelty seeking ($54.11 ± 2.47$); low self-transcendence ($42.37 ± 3.43$). MBTI primary profile: ESFJ ($16.7\%).\n - *Physiotherapy (4$studies,$n = 495$)*: High agreeableness ($3.75 ± 0.03$), conscientiousness ($3.69 ± 0.00$), extroversion ($3.49 ± 0.00$), openness ($3.42 ± 0.00$); low neuroticism ($2.38 ± 0.00$);$66\%$Sensing-Judging ($\text{SJ}$); higher achievement strivings ($3.5 ± 0.44$) than impatience-irritability ($2.4 ± 0.57).\n - *Pharmacy (2$studies,$n = 298)*: High agreeableness, extroversion, openness, responsibility.\n - *Paramedics (3$studies,$n = 744$)*: Highest conscientiousness, lowest neuroticism. Age differences: paramedics aged$< 35$years showed greater mental balance, extraversion, and masculinity-femininity than those aged$\ge 45 years; older paramedics showed lower spontaneous aggression.\n - *Pathologists (1$study,$n = 37$)*: High extraversion ($3.98 ± 0.73$) and agreeableness ($3.97 ± 1.09).\n - *Radiologists (1$study,$n = 117$)*: High hardiness ($89.9 ± 11.9).\n - *Social Workers (1$study,$n = 8$)*: Lower neuroticism ($11.55 ± 2.16) relative to physicians.\n\n- **Behaviour Style Profiling Across Disciplines**\n - Evaluated in 10$studies ($n = 6709$participants;$7$meta-aggregated,$3 narrative).\n - *Nursing (5$studies,$n = 5464$)*: Mixed Type A and Type B patterns; Type A urgency aligns with high dominance ($39\%$) and conscientiousness ($35\%).\n - *Medicine (3$studies,$n = 742$)*: Equal distribution of Type A ($33.1\%$), Type B ($33.8\%$), and intermediate ($33.1\%$) styles. DiSC profiling: Conscientiousness ($5.14 ± 0.16$), Steadiness ($4.93 ± 0.00$), Dominance ($2.90 ± 0.14$), Influence ($3.63 ± 0.00).\n - *Occupational Therapy (1 study)*: Role distinction—clinicians align with lovingness, mature love, and inner harmony; administrators align with capability, operational control, and accomplishment.\n - *Psychology (1$study)*: Male research psychologists show elevated Type A achievement striving ($10.3 ± 3.4).\n\n- **Emotional Intelligence (\text{EI}) Profiling Across Disciplines**\n - Evaluated in 146$studies ($n = 42,795$participants;$142 meta-aggregated).\n - *General Trend*: Health professionals consistently exhibit average to above-average global \text{EI} scores across standardized instruments (e.g., MSCEIT, Schutte Self-Report, TEIQue, Bar-On EQ-i, WLEIS).\n - *Nursing (105$studies)*: Low to average global$\text{EI}, but exceptionally high scores in interpersonal relationships, emotionality, commitment, and altruistic patient care.\n - *Medicine (33$studies)*: Average to above-average global$\text{EI} with high self-control, high natural acting emotional labor, and strong intrapersonal skills, but lower scores in general mood, stress management, and perceived benefit from emotions.\n - *Dentistry (3$studies,$n = 661$)*: High$\text{EI} and high empathy.\n - *Occupational Therapy (3$studies,$n = 1369$)*: Above-average$\text{EI}$(Swinburne emotional understanding$78.46 ± 8.24$; TEIQue-SF well-being$5.79 ± 0.82$, self-control$4.93 ± 0.88$, emotionality$5.76 ± 0.73$, sociability$5.07 ± 0.78).\n - *Physiotherapy (2$studies,$n = 189$)*: Moderate Genos$\text{EI}$score ($129.36 ± 18.314), negatively correlated with occupational stress.\n - *Radiology (1$study,$n = 22$)*: High global and subscale$\text{EI}$($5.15–6.25).\n\n# Trait Emotional Intelligence, Stress, Burnout, and Engagement in Academic Contexts\n\n- **Conceptual Model & Empirical Objectives**\n - Trait Emotional Intelligence (\text{TEI}) is defined as a constellation of emotional self-perceptions and behavioural dispositions located at lower levels of personality hierarchies.\n - \text{TEI}$incorporates$15 factors spanning four overarching domains: Emotionality, Self-control, Sociability, and Well-being.\n - Academic Engagement is a multidimensional construct comprising four distinct operational facets:\n - *Behavioral Engagement*: Effort, attention, active classroom participation, and concentration.\n - *Cognitive Engagement*: Self-regulated learning, deep learning strategies, and conceptual synthesis.\n - *Emotional Engagement*: Positive affective reactions (interest, curiosity, enjoyment, satisfaction).\n - *Agentic Engagement*: Proactive, intentional contributions to learning environments (offering input, expressing preferences, asking questions).\n - Academic Burnout comprises three distinct dimensions: Exhaustion (feeling overwhelmed and emotionally drained by schoolwork), Cynicism (detached, indifferent attitudes toward study), and Inadequacy (reduced sense of academic accomplishment and self-efficacy).\n - The longitudinal study evaluated how \text{TEI}$predicts academic stress, burnout, and engagement across a full academic semester among Japanese undergraduate$\text{EFL}$learners ($N = 184).\n\n- **Cohort & Assessment Characteristics**\n - Sample size: N = 184$undergraduate students ($87$females,$91$males,$6$unstated; mean age$19.70 ± 1.31 years) at a private university in Tokyo, Japan.\n - Educational background: Mean length of English study = 9.31 ± 2.98$years;$20.5\%$studied abroad in an English-speaking country (mean duration$69.22 ± 92.77$days at mean age$17.41 ± 3.24$years);$57.6\%$visited/lived in English-speaking countries (mean duration$544.82 ± 1522.15 days).\n - Time allocation: Mean course-related English study = 298.52 ± 275.73$\,min/week; non-course English study =$94.49 ± 167.72\,min/week.\n - Standardised language proficiency distribution: TOEFL iBT (n = 25$, mean$78.44 ± 17.87$), TOEFL ITP ($n = 70$, mean$526.23 ± 35.22$), WeTEC ($n = 73$, mean$730.95 ± 88.82$), TOEIC ($n = 36$, mean$817.36 ± 85.16$), EIKEN ($n = 48$, mean$1.71 ± 0.34$), IELTS ($n = 3$, mean$6.16 ± 0.57$). CEFR band breakdown ($n = 49$): A2 =$2.2\%$, B1 =$33.2\%$, B2 =$34.2\%$, C1 =$3.8\%.\n\n- **Measurement Instruments & Psychometric Reliability**\n - *Trait Emotional Intelligence Questionnaire (\text{TEIQue}$)*:$26$items,$7$-point Likert scale. Subscales: Self-control (Cronbach's$\alpha = 0.70$), Well-being ($\alpha = 0.81$), Sociability ($\alpha = 0.71$), Emotionality ($\alpha = 0.60$); total instrument$\alpha = 0.77.\n - *Perceptions of Academic Stress Scale (\text{PAS}$)*:$13$items,$5$-point Likert scale ($\alpha = 0.85$; baseline total$\alpha = 0.73).\n - *School Burnout Inventory (\text{SBI}$)*:$9$items,$6$-point Likert scale. Subscales: Cynicism ($3$items,$\alpha = 0.67$), Exhaustion ($4$items,$\alpha = 0.80$), Inadequacy ($2$items); total instrument$\alpha = 0.84$.

  • Academic Engagement Scale: $19$ items, $6$-point Likert scale. Subscales: Behavioral ($5$ items, $\alpha = 0.92$), Emotional ($4$ items, $\alpha = 0.90$), Cognitive ($8$ items, $\alpha = 0.88$), Agentic ($5$ items, $\alpha = 0.85$); total instrument \alpha = 0.81$.\n\n- **Descriptive Statistics and Bivariate Correlation Matrix**\n - Trait \text{EI}$: Mean$3.577 ± 0.713$,$\text{95\%}\,\text{CI} = [3.47, 3.68]\n - Academic Stress: Mean 3.175 ± 0.833$,$\text{95\%}\,\text{CI} = [3.05, 3.30]\n - Burnout: Mean 3.050 ± 1.001$,$\text{95\%}\,\text{CI} = [2.90, 3.20]\n - Engagement: Mean 3.445 ± 0.838$,$\text{95\%}\,\text{CI} = [3.32, 3.57]\n - Bivariate Pearson Correlations:\n - \text{TEI}$and Academic Stress:$r = -0.523$($p < 0.001)\n - \text{TEI}$and Burnout:$r = -0.476$($p < 0.001)\n - \text{TEI}$and Engagement:$r = 0.248$($p < 0.05)\n - Stress and Burnout: r = 0.823$($p < 0.001)\n - Stress and Engagement: r = -0.099 (non-significant)\n - Burnout and Engagement: r = -0.226$($p < 0.05)\n\n# Structural Equation Modeling and Cluster Trajectory Analysis\n\n- **Hypothesised Model**\n - Structural Equation Modeling (\text{SEM}$) via Maximum Likelihood estimation with bias-corrected bootstrapping ($\text{10,000}$resamples) and Full Information Maximum Likelihood ($\text{FIML}$) for missing data evaluated the structural relationships among$\text{TEI}, Stress, Burnout, and Engagement.\n\n\n\n- **Fitted Structural Path Model**\n - Model fit statistics: \chi^2(59) = 135.912$,$p < 0.001$;$\text{RMSEA} = 0.085$($\text{95\%}\,\text{CI} = [0.066, 0.103]$);$\text{CFI} = 0.918$;$\text{AIC} = 5628.986$;$\text{BIC} = 5773.167.\n - Standardised Path Coefficients (b):\n - \text{TEI} \rightarrow \text{Academic Stress}$:$b = -0.52$,$p < 0.001 (significant negative prediction)\n - \text{TEI} \rightarrow \text{Engagement}$:$b = 0.24$,$p = 0.049 (significant positive prediction)\n - \text{TEI} \rightarrow \text{Burnout}$:$b = -0.06$,$p = 0.521 (non-significant direct path)\n - \text{Academic Stress} \rightarrow \text{Burnout}$:$b = 0.79$,$p < 0.001 (strong positive prediction)\n - \text{Academic Stress} \rightarrow \text{Engagement}$:$b = 0.36$,$p = 0.107 (non-significant path)\n - \text{Burnout} \rightarrow \text{Engagement}$:$b = -0.41$,$p = 0.062 (non-significant path)\n - Direct and Indirect Path Summary: \text{TEI}$directly increases engagement and directly suppresses academic stress. Academic stress strongly drives academic burnout.$\text{TEI} indirectly buffers burnout through the mediation of academic stress.\n\n\n\n- **K-Means Cluster Profiling**\n - Participants grouped into two distinct \text{TEI}$profiles using K-means cluster analysis on subscale scores: High$\text{TEI}$($n = 85$) and Low$\text{TEI}$($n = 93).\n - Cluster validation: Subsample replication demonstrated Cohen's Kappa \kappa = 0.82$($91\% classification agreement).\n - Cluster Center Profiles:\n - Emotionality: High \text{TEI} = 4.48$; Low$\text{TEI} = 3.47\n - Well-being: High \text{TEI} = 4.53$; Low$\text{TEI} = 2.97\n - Self-control: High \text{TEI} = 3.76$; Low$\text{TEI} = 2.84\n - Sociability: High \text{TEI} = 3.85$; Low$\text{TEI} = 2.91\n\n- **Repeated Measures ANOVA: Longitudinal Trajectories (Baseline to Follow-up)**\n - *Academic Stress*:\n - Between-subjects: F(1, 162) = 23.48$,$p < 0.001$,$\eta^2 = 0.10.\n - High \text{TEI}$: Baseline Mean =$2.86 ± 0.87$; Follow-up Mean =$2.87 ± 1.11.\n - Low \text{TEI}$: Baseline Mean =$3.40 ± 0.81$; Follow-up Mean =$3.51 ± 0.95.\n - *Cynicism*:\n - Between-subjects: F(1, 163) = 20.45$,$p < 0.001$,$\eta^2 = 0.08.\n - High \text{TEI}$: Baseline Mean =$2.70 ± 0.99$; Follow-up Mean =$2.70 ± 1.11.\n - Low \text{TEI}$: Baseline Mean =$3.43 ± 1.13$; Follow-up Mean =$3.18 ± 0.98.\n - *Exhaustion*:\n - Between-subjects: F(1, 165) = 3.96$,$p = 0.04$,$\eta^2 = 0.02.\n - Within-subjects (Time effect): F(1, 165) = 4.09$,$p = 0.04$,$\eta^2 = 0.01$.

  • High $\text{TEI}$: Baseline Mean = $2.32 ± 1.16$; Follow-up Mean = $2.45 ± 0.97$ (stable).

  • Low $\text{TEI}$: Baseline Mean = $2.52 ± 1.04$; Follow-up Mean = $2.78 ± 1.03$ (statistically significant increase over the semester).

  • Inadequacy:

  • Between-subjects: $F(1, 163) = 22.24$, $p < 0.001$, \eta^2 = 0.09$.\n - Within-subjects (Time effect): F(1, 163) = 3.10$,$p = 0.08$, \eta^2 = 0.00$.\n - High \text{TEI}$: Baseline Mean =$3.46 ± 1.25$; Follow-up Mean =$3.12 ± 1.22 (statistically significant decrease over the semester).\n - Low \text{TEI}$: Baseline Mean =$4.09 ± 1.32$; Follow-up Mean =$4.05 ± 1.31 (elevated and persistent).\n - *Agentic Engagement*:\n - Between-subjects: F(1, 165) = 10.26$,$p < 0.01$,$\eta^2 = 0.04$.

  • High $\text{TEI}$: Baseline Mean = $2.39 ± 1.00$; Follow-up Mean = $2.37 ± 1.03$.

  • Low $\text{TEI}$: Baseline Mean = $1.93 ± 0.85$; Follow-up Mean = $2.01 ± 1.04$.

  • Emotional Engagement:

  • Between-subjects: $F(1, 165) = 6.92$, $p < 0.01$, \eta^2 = 0.03$.\n - Within-subjects (Time effect): F(1, 165) = 9.06$,$p < 0.01$,$\eta^2 = 0.01$.

  • High $\text{TEI}$: Baseline Mean = $3.71 ± 1.08$; Follow-up Mean = $4.05 ± 1.13$ (statistically significant longitudinal increase).

  • Low $\text{TEI}$: Baseline Mean = $3.41 ± 1.12$; Follow-up Mean = $3.56 ± 1.09$.

  • Behavioral & Cognitive Engagement:

  • Behavioral: Between-subjects $F(1, 165) = 1.06$, $p = 0.31$; Within-subjects $F(1, 165) = 1.36$, $p = 0.25$ (non-significant differences).

  • Cognitive: Between-subjects $F(1, 165) = 1.27$, $p = 0.26$; Within-subjects $F(1, 165) = 3.06$, $p = 0.08$ (non-significant differences).

• Educational & Interventional Implications

  • High $\text{TEI}$ acts as a buffer against semester-long fatigue and emotional depletion, preventing the accumulation of academic exhaustion.

  • Students with higher $\text{TEI}$ experience longitudinal growth in affective learning connection (emotional engagement) and reductions in perceived inadequacy over the academic term.

  • Target explicit $\text{TEI}$ skill training (focusing on self-control, emotion regulation, sociality, and optimism) in academic curricula to interrupt stress-burnout spirals and sustain academic engagement.