What makes physicians thrive? Assessing the mental health of Hungarian physicians with the Stanford Professional Fulfillment Index

What makes physicians thrive? 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Assessing the mental health of Hungarian physicians with the Stanford Professional Fulfillment Index Orsolya Gyöngyösi, Tamás Martos, Ágnes Hegedűs, Viola Sallay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6275614/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and aims : The mental health of physicians - and Hungarian physicians in particular - is a pressing issue and understanding the factors involved is of paramount importance. Professional fulfillment is a concept that looks at the mental health of physicians in a non-traditional way and has been studied little in this context. In Hungary, there was no instrument in the past that investigated the factors involved in the mental health of physicians, so in our research we focused on the exploration of fulfillment. We conducted a validation study with Hungarian physicians using the Hungarian version of the Stanford Professional Fulfillment Index (SPFI-H) . Moreover, we tested the potential influencing factors of professional fulfillment in relation to the physicians’ well-being and burnout. Methods : Physicians (n = 256) responded to an online questionary, containing SPFI-H, Mini Oldenburg Burnout Questionnaire – MOLBI, Work and Meaning Inventory – WAMI, Subjective rated health status – SRH, Satisfaction with Life Scale – SWLS and Multidimensional Work Motivation Scale - MWMS. The psychometric testing of the SPFI-H included internal consistency, convergent validity, discriminant validity, and construct validity with confirmatory factor analysis (CFA). Maximum likelihood parameter estimator with robust standard error estimation (MLR) was used for the CFA. Statistical analysis was performed using hierarchical (blockwise model) multiple regression analysis. Results : Confirmatory factor analysis of the original factor structure showed that the structural model was a poor fit. After omitting two items with low standardized coefficients, the final, 14 item model’s fit was found to be good, with all coefficients being sufficiently high (ß>0.53) and the fit indices indicated also adequate fit (χ 2 =222.38, df=72, χ 2 /df=3.09 CFI=.94, IFI=.94, TLI=.92, RMSEA=.09 CI of RMSEA=.07-.10, SRMR=.05). The hierarchical multiple regression analysis revealed that intrinsic motivation, meaningful work and positive meaning positively predicted professional fulfillment with 59% of the total explained variance. Conclusion : SPFI-H scales have adequate psychometric characteristics and offer a novel avenue for assessing professional fulfillment. This may extend our current knowledge on physicians’ well-being and inspire future investigations beyond focusing solely on burnout. professional fulfillment physician well-being burnout validation confirmatory factor analysis Figures Figure 1 INTRODUCTION Physicians’ well-being has an emerging focus in recent research, whereas the medical community increasingly acknowledges its pivotal role in physicians’ job performance and health, workplace climate and physician-patient relationships ( 1 , 2 ). Physicians' wellness (used interchangeably with physician well-being) is a complex construct that includes physicians' physical, mental, and emotional health. Fulfillment is a more intrinsic, deeper sense of purpose and satisfaction in life which contributes to well-being. Although increasing importance is being attached to physicians’ well-being, little effort has been made to define the construct, resulting in a lack of agreement on how physician well-being should be understood and assessed ( 3 ). In this paper, our goal is to understand what factors affect the professional fulfillment of Hungarian physicians. Accordingly, in the first part, we present a validation study regarding physicians’ well-being assessment performed with Hungarian physicians using the Stanford Professional Fulfillment Index. In the second part, we performed hierarchical multiple regression with the SPFI-H to explore the relationship between potential influencing factors and fulfillment. Physicians’ well-being Well-being itself cannot be easily defined, which is well shown by the fact that Brady and colleagues were able to collect more than ten different explicit physician well-being definitions ( 4 ). Most definitions describe the following aspects of physician well-being: emotional health, physical health, feeling challenged, spiritual well-being, work-life balance, thriving, authentic expressiveness and vigor. In contrast, the negative aspect of physician's well-being – unwellness - describes conditions that make it difficult to concentrate on work and that can ultimately lead to burnout. Such is emotional exhaustion fatigue , anxiety, depression , or amotivation. Furthermore, lesser-known factors are important in terms of both wellness and unwellness, thus can be considered ambivalent. Such as: motivation to work, work as meaning, emotional vulnerability or experiencing embeddedness in the hierarchical system of healthcare institutions. The Hungarian context: Physicians’ well-being in Hungary In 2022, Spányik et al. performed a countrywide cross-sectional survey among healthcare workers (HCW), focusing on the mental health status (including depression and burnout syndrome) of HCWs and its possible predictors related to the COVID-19 outbreak ( 5 ). They assessed the mental health status related factors by Perceived Stress Scale (PSS). Work-related stress was assessed using the Professional Quality of Life Scale. Emotional exhaustion factor of burnout was assessed using the Emotional Exhaustion subscale of Maslach Burnout Inventory for Human Service Survey (MBI EE). Depression was assessed using the shortened (9 items) Hungarian version of the Beck Depression Inventory (BDI). According to their findings, both perceived and work-related stress exacerbated burnout. There was no direct relationship observed between COVID-19-related objective factors (e.g., working on the front lines or having to work in another department during the pandemic) and stress, burnout, or depression. Their findings suggest that the pandemic's challenges had only a mediated effect because stressors exert their influence solely through subjective perception. This underlines the significance of mental health assistance among healthcare workers. Previous studies with SPFI Since its publication in 2018, several studies have used the SPFI to assess burnout and fulfillment in physicians. For instance, Vetter and colleagues tested the SPFI as a stand-alone instrument to assess different aspects of physicians’ well-being. They found all SPFI subscales significantly correlated with all BPI subscales and both single-item Burnout instruments. Self-reported medical errors had a low correlation with the SPFI interpersonal disengagement subscale and did not correlate with work exhaustion and professional fulfillment subscale. This indicates that SPFI, a simple 16-item questionnaire, could be a single instrument of assessing the well-being of physicians, moreover the measuring could be replicable ( 6 ). Recently, Lu and colleagues conducted a cross-sectional survey of 771 Society for Academic Emergency Medicine (SAEM) members. They added two new items to the interpersonal disengagement subscale ("Less empathetic with trainees" and "More likely to be impatient or terse with clinical support staff") which account for additional aspects of depersonalization that may be prevalent in academic emergency medicine. Cronbach's alpha for each of the nine domains ranged from 0.77 to 0.91( 7 ). Eckstein and colleagues examined how burnout is related to mindfulness, fulfillment, specialty choice, and other lifestyle factors in a multidisciplinary population, including radiation oncology. They performed a survey with three sections (mindfulness assessment, personal and lifestyle factors hypothesized to be related to fulfillment and SPFI). They found that mindfulness positively correlated with fulfillment, negatively correlated with exhaustion and interpersonal disengagement. Overall, their results confirmed that SPFI is an easy-to-use, quick-to-complete questionnaire, of which the reliability and validity have been demonstrated in previous studies ( 8 ). Language adaptations To this day, SPFI was adapted only in two languages. The Brazilian version was adapted to a sample of 432 physicians (n = 432) who performed activities in the field of occupational medicine. Silva et al performed exploratory and confirmatory factor analysis. Reliability and validity indicators were good in their sample (Cronbach’s alpha (.95), McDonald’s omega (.95), and ORION (greatest lower bound) (.98) and the three-factor structure found by the original authors was replicated ( 9 ). The Japanese version, (n = 666) also found a three-factor structure and proved to be reliable and valid. Cronbach's alphas of PFI subscales was 0.91 in professional fulfillment, 0.80 in burnout: work exhaustion, 0.90 in burnout: interpersonal disengagement, and 0.89 in burnout ( 10 ). Aims of the study The aim of our study was to bring the mental health factors of physician’s well-being (general and characteristic of Hungary) into focus. In order to better understand the complex structure of physicians’ well-being, we are introducing the SPFI-H: an instrument to measure physician’s well-being. We present a study performed in Hungary which applies the SPFI on a sample of the Hungarian physician population. Moreover, we performed hierarchical multiple regression with the SPFI-H to explore the relationship between different predictors and the experience of fulfillment. In the following, we outline the psychometric characteristics of our study. METHODS Procedure The current study was a cross-sectional survey, the sample consisted of physicians who have at least a degree in medicine and are currently pursuing medical practice. The recruitment period started on 18 January 2023, ended on 14 June 2023. The participants were reached by means of an online questionnaire. The questionnaire study was created using Lime Survey. Using purposive sampling, we sent a link of the questionnaire directly to individuals who met the criteria and asked them to share it further among medical professional groups (e.g. medical Facebook groups such as Hungarian Medical Chamber). All questions were obligatory; thus, we excluded partial answers. Ethical approval and consent to participate All participants provided informed consent before taking part in the study. Prior to participation, they received detailed information regarding the study’s purpose, procedures, potential risks, and their right to withdraw at any time without consequences and that the data would be processed and published in the form of aggregated statistical results. Participants indicated their consent by acknowledgment in the initial part of the survey, in compliance with ethical guidelines outlined by the Ethical Board of the Hungarian Council of Health Sciences (nr. 2023-03). The investigation conforms to the principles outlined in the Declaration of Helsinki. Sample Based on the guideline, confirmatory factor analysis requires a minimum of 10–15 respondents per item, therefore, for current research, the required size is 240 individuals ( 11 ). A total of 458 participants opened the questionnaire and 256 participants completed it. The sample included 179 females (69.1%) and 77 males (30.8%). The mean age of the participants was 44.3 years (SD = 12.6, min = 24, max = 78). We asked the participants about their current place of residence, their marital status, their level of qualifications, their work experience in healthcare, the type of healthcare institution in which they work and the current number of positions they hold as a medical professional (Table 2 ). Table 2 Demographic characteristics of the sample Sample characteristics N (%) Place of residence capital 115 (44.9) city with county rights 75 (29.3) town 46 (18.0) village/municipality 20 (7.8) Level of qualification intern 8 (3.1) resident 18 (7.0) fellow 27 (10.5) specialist 125 (48.8) specialist with more than 1 specialty 77 (30.1) Marital status single 42 (16.4) in a relationship 66 (25.8) married 144 (56.3) other/NA 4 (1.6) Years spent working in healthcare 0–5 years 36 (70.6) 6–10 years 8 (15.7) 11–15 years 2 (3.9) 16–20 years 1 (2.0) more, than 21 years 4 (7.8) Number of current positions One 113 (44.1) Two 85 (33.2) Three 4 (17.2) more, than three 14 (5.5) Type of healthcare provider (multiple choice possible) hospital 145 (56.6) GP practice 115 (44.9) private healthcare provider 60 (23.4) specialist outpatient clinic 19 (7.4) outside of Hungary 6 (2.3) Other 45 (17.6) Given that the inclusion criteria were having at least a degree in medicine and currently pursuing medical practice, we also asked about the type of specialty(ies). The sample’s detailed distribution of the specialties is given in Table 3 . Table 3 Frequency data of type of specialty of the sample. Type of specialty (multiple choice possible) N (%) Paediatrics 69 (19.7) Family medicine 52 (14.9) I do not have a specialty 51 (14.6) Emergency medicine 22 (6.3) Internal medicine 18 (5.1) Occupational medicine 12 (3.4) School health and youth protection 11 (3.1) Psychotherapy 11 (3.1) Psychiatry 8 (2.3) Vascular surgery 7 (2.0) Gastroenterology 7 (2.0) Neurology 6 (1.7) Cardiology 5 (1.4) Neonatology 5 (1.4) Radiology 5 (1.4) Obstetrics and Gynaecology 5 (1.4) Ear, nose and throat medicine; Surgery; Urology; Allergology and clinical immunology; Endocrinology and metabolic diseases; Nephrology; Sports medicine; Anaesthesiology and intensive care; Andrology; Addictology; Maxillo-facial and oral surgery; Haematology; Disaster medicine; Orthopaedics and traumatology; Medical rehabilitation; Audiology; Dermatology; Paediatric surgery; Forensic medicine; Infectiology; Hand surgery; Clinical neurophysiology; Clinical pharmacology; Preventive medicine and public health; Public health and hygiene; Pathology; Rehabilitation medicine; Rheumatology; Ophthalmology; Transfusiology; Pulmonary medicine; Each of these categories had n < = 4 (1.2%) respondents indicating them. Instruments The study used Stanford Professional Fulfillment Index (SPFI). SPFI contains 16 + 4 items assessing experiences of professional fulfillment over the previous 2 weeks and it is divided into two main sections measuring the Professional Fulfillment Index and the Self-reported Medical Errors constructs. The Professional Fulfillment Index contains three subscales, including (a) professional fulfillment (six items; e.g., “I feel happy at work”), (b) job exhaustion (four items; e.g., “During the past two weeks I have felt a sense of dread when I think about work, I have to do”), and (c) interpersonal disengagement (six items; e.g., “During the past two weeks, my job has contributed to me feeling less empathetic with my patients”). The questionnaire does not contain reverse items and items are rated on a five-point Likert scale (0 = not at all, 4 = completely true). The scale is scored by summing the responses to the items, with a higher score indicating higher experience concerning the respective constructions. In the Self-reported Medical Errors section (four items), the respondents rated the recent occurrence of specific medical errors statements (e.g., “I made a major medical error that could have resulted in patient harm”) in their practice on a six-point Likert scale (0 = never, 5 = in the last week). Trockel et al. calculated Cronbach alpha of each PFI scale to estimate internal consistency reliability. Internal and test-retest reliability was good, for professional fulfillment, Cronbach’s α = .91, r = .82; for work exhaustion Cronbach’s α = .86, r = .71; for interpersonal disengagement Cronbach’s α = .92, r = .80. The internal consistency reliability estimate for the self-reported medical error scale was α = .62 ( 12 ) Mini Oldenburg Burnout Questionnaire – MOLBI. Based on the Oldenburg Burnout Questionnaire (OLBI), a shortened version Mini Oldenburg Burnout Questionnaire (MOLBI) was published in Hungarian to assess the symptoms of burnout. The questionnaire consists of 10 items and has two subscales, exhaustion and disengagement. Five items belong to each scale, and two of them are reversed. Responses are scored on a four-point Likert scale (1 = strongly agree, 4 = strongly disagree), with higher scores indicating higher levels of burnout. Reliability indicators for the questionnaire used in the current study: for exhaustion Cronbach’s α = .51, for disengagement Cronbach’s α = .67 ( 13 ) Work and Meaning Inventory – WAMI. The questionnaire was developed by Steger et al. ( 14 ). The original version measures job meaningfulness through three dimensions: “Positive Meaning”, “Meaning Making through Work”, and “Greater Good Motivations”. The questionnaire consists of 10 items rated on a 5-point Likert scale (1 = absolutely untrue, 5 = absolutely true). The questionnaire proved valid and reliable for both the original and the Hungarian versions ( 15 ). Reliability indicators for the questionnaire used in the current study are Cronbach’s α = .82, .73, and .72, respectively. Subjective rated health status - SRH . We measured the respondent's evaluation of their health status with the question "Overall, how would you rate your health?" The five response options ranged from very poor to excellent ( 16 ). Satisfaction with Life Scale - SWLS . The SWLS is a five-item questionnaire measuring a general evaluation of one’s life (e.g., “If I could live my life over, I would change almost nothing”). Respondents can indicate their level of agreement with statements on a seven-point scale (1 = strongly disagree, 7 = strongly agree). The scale score is the sum of the responses to the items, with a higher score indicating higher life satisfaction ( 17 ). The questionnaire proved valid and reliable for both the original and the Hungarian versions ( 18 ). Reliability indicator for the questionnaire used in the current study is Cronbach’s α = .88 The Multidimensional Work Motivation Scale - MWMS . We used the validated Hungarian version of MWMS ( 19 ). When completing the questionnaire, respondents were asked to rate the extent to which each statement describes them on a seven-point scale (1 = not at all; 7 = completely). The question asks why the respondent makes an effort and puts energy into their work (i.e. what motivates them). The instrument contains six subscales such as, Amotivation, Extrinsic regulation - social, Extrinsic regulation – material, Introjected regulation, Identified regulation, and Intrinsic regulation. Each subscale has 3 items, except for Introjected regulation, which has 4 items. Higher scores on a specific subscale indicate stronger work motivation of the kind. Reliability indicators for the questionnaire used in the current study are Cronbach’s α = .78, .74, .83, .74, .83, and .90, respectively. Data analysis During the validation process, the first step was to translate the original questionnaire to Hungarian by two independent professionals who were experts in the field (the English Hungarian translated version see in appendix). The translations were then discussed and used to create a consensual version for back-translation. Back-translation was made by a native American English speaker, bilingual (English - Hungarian) physician. Expert inspection of the back-translated text confirmed that it conveyed the same meaning as the original. During the data analysis, we conducted a descriptive analysis, looking at frequencies, percentage distributions, means and standard deviations. After reporting the demographics, we carried out a confirmatory factor analysis (CFA) to test the model fit of the Professional Fulfillment Index section of the SPFI questionnaire. According to the original model, we defined three latent variables: professional fulfillment, work exhaustion, and interpersonal disengagement. Maximum likelihood parameter estimator with robust standard error estimation (MLR) was used for the CFA as we assumed that data were not normally distributed. The model fit assessment was based on the fit indices with the respective cut-off values: chi-square (χ²) (p-value > .05), normed χ²/df < 3.0, comparative fit index (CFI) and Tucker–Lewis fit index (TLI) of ≥ .95 (good) or ≥ .90 (acceptable), root mean square error of approximation (RMSEA) of ≤ .08 and standardized root mean square residual (SRMR) of ≤ .08 ( 20 ). Since the fit of the original model did not reach an acceptable level, we analyzed modification indices to remove potential items with cross-loadings and to allow reasonable residual covariances between items loading to the same factor. Finally, we checked the explained variances and factor loadings of the items, and we excluded items with R 2 < 25% and λ < .50. An additional CFA was carried out to test the internal structure of the Self-reported Medical Errors section of the SPFI questionnaire. The reliability was tested using Cronbach’s alpha. The suggested cut-off values for Cronbach’s alpha were excellent (0.90 and above), high (0.70–0.90), moderate (0.50–0.70), and low (0.50 and below). The scale scores were calculated by summing up the values of the corresponding items. We reported the descriptive statistics of the scales (mean, standard deviation, minimum, maximum). Normality of the subscales was measured using skewness and kurtosis values. Based on the results of normality testing, Pearson or Spearman correlation was used to examine the correlations between the SPFI subscales and the associations with WAMI, MWMS and SWLS. Independent sample t-test or Mann-Whitney U-test were used to determine gender differences. Spearman correlations were used to measure the association with age, place of residency, level of qualification, years spent working in healthcare, number of current positions and the dichotomous variables of type of healthcare provider. We performed a hierarchical multiple regression to predict fulfillment based on seven independent variables: gender, age, level of qualification, different facets of work motivations and positive meaning in work. Predictors were included in the model based on prior research and theoretical relevance. The model fit was assessed using R-squared and adjusted R-squared values, and the statistical significance of individual predictors was evaluated with p-values. All assumptions of multiple linear regression, including linearity, homoscedasticity, and normality of residuals, were assessed through diagnostic plots. Multicollinearity was checked using variance inflation factors (VIFs). The regression model is specified as follows: Fulfillment = β 0 + β 1 (MWMS extrinsic social) +β 2 (MWMS extrinsic material) + β 3 (MWMS intrinsic motivation) + β 4 (WAMI positive meaning). All analyses were performed using JASP 0.19.1; (JASP Team, 2024). RESULTS Confirmatory Factor Analysis (CFA) results for SPFI-H questionnaire The CFA showed that the model of the original 16 item Professional Fulfillment Index had a poor fit (Model 1 in Table 4 ). The model’s misfit was improved by considering the residual covariances justified to theoretical and methodological reasons. Based on the modification indices, we performed a stepwise modification process to achieve adequate fit while preserving the model’s theoretical integrity. In Model 2 and 3, we included residual covariances between the items of Interpersonal Disengagement 2 and 6 (“ During the past two weeks my job has contributed to me feeling less empathetic with my colleagues” and “ During the past two weeks my job has contributed to me feeling less connected with my colleagues ”) and between the items of Interpersonal Disengagement 4 and 5 (“ During the past two weeks my job has contributed to me feeling less interested in talking with my patients ” and “ During the past two weeks my job has contributed to me feeling less connected with my patients ”) (Model 2 and 3, respectively). We also omitted the items of Professional Fulfillment 6 and 4 from the model (“ I’m contributing professionally (e.g. patient care, teaching, research, and leadership) in the ways I value most ” and “ I feel in control when dealing with difficult problems at work ”), because the explained variance and the factor loadings of these items were low (R² = .22 and .23 and λ = .46 and .46 respectively) (Model 4 and 5, respectively). Table 4 Summary of fit indices for confirmatory factor analysis of Hungarian adaptation of Stanford Professional Fulfillment Index (SPFI-H) Model χ² df χ²/df CFI IFI RMSEA CI of RMSEA SRMR Model1 461.81 101 4.57 .87 .87 .12 .11-.13 .07 Model2 333.14 100 3.33 .91 .91 .10 .08-.11 .07 Model3 304.34 99 3.07 .92 .92 .09 .08-.10 .07 Model4 280.91 86 3.30 .93 .93 .09 .08-.11 .06 Model5 222.38 72 3.09 .94 .94 .09 .07-.10 .05 Note. Model 1: original factor structure; Model 2: Residual covariance between Disengagement 2 and Disengagement 6 introduced; Model3: Residual covariance between Disengagement 4 and Disengagement 5 introduced; Model4: Fulfillment 6 removed; Model5: Fulfillment 4 removed, FINAL MODEL The final model showed adequate fit indices (Model 5 in Table 4 ), and all the items had acceptably high factor loadings on their respective constructs (Table 5 ). See Fig. 1 for visual representation. Table 5 Factor loadings of Stanford Professional Fulfillment Index Factor Item λ SE z-value p Confidence interval Professional Fulfillment Fulfill 1 0.79 0.06 14.17 < .001 0.68 0.89 Fulfill 2 0.70 0.06 11.97 < .001 0.59 0.81 Fulfill 3 0.81 0.06 13.27 < .001 0.69 0.93 Fulfill 5 0.66 0.06 10.40 < .001 0.53 0.78 Work Exhaustion Workexh 1 0.52 0.06 8.11 < .001 0.40 0.65 Workexh 2 0.85 0.07 12.42 < .001 0.72 0.99 Workexh 3 1.12 0.05 22.69 < .001 1.02 1.22 Workexh 4 1.18 0.05 23.02 < .001 1.08 1.28 Interpersonal Disengagement Diseng 1 1.08 0.05 19.66 < .001 0.97 1.18 Diseng 2 0.81 0.08 10.79 < .001 0.66 0.96 Diseng 3 0.98 0.07 14.74 < .001 0.85 1.11 Diseng 4 1.07 0.06 18.89 < .001 0.95 1.18 Diseng 5 1.10 0.06 18.07 < .001 0.98 1.22 Diseng 6 0.72 0.08 9.57 < .001 0.57 0.87 An additional CFA was performed to assess the structural validity of the Self-reported Medical Errors scale. Relative fit indices indicated good fit (CFI = .91 and IFI = .91) but chi-squared test and RMSEA indicated a poor fit (χ² ( 2 ) = 25.69 p < .001, RMSEA = .21 [CI = .15-.29]. The explained variances and factor loadings of all four items were acceptable (R² values were between .30 and 62 and λ values were between .52 and .84). Therefore, we decided to retain the subscale. Reliability analysis Reliability was assessed using Cronbach's alpha values. The coefficients obtained indicated good to excellent reliability of the subscale. The Cronbach's alpha for the professional fulfillment subscale of the SPFI-H questionnaire was .83, for work exhaustion was .83, for interpersonal disengagement was .91, and for the Self-reported Medical Error scale was 0.74. Descriptive statistics of the subscales Descriptive statistics of the participants' SPFI scores are shown in Table 6 . In terms of skewness and kurtosis, professional fulfillment, work exhaustion and interpersonal disengagement subscales follow approximately normal distributions. In contrast, for medical errors, there was a significant positive skew, i.e. a significant proportion of participants reported low levels of error in their work. Table 6 Descriptive statistics of the SPFI-H subscales Subscales M SD Skew Kurt Min Max Professional Fulfillment 11.25 3.21 -0.84 0.80 0 16 Work Exhaustion 6.30 4.02 0.31 -0.88 0 16 Interpersonal Disengagement 7.36 6.16 0.77 -0.18 0 24 Self-reported Medical Errors 3.20 3.16 1.90 4.95 0 20 Using Pearson correlations, we found a significant, strong negative association between fulfillment and exhaustion (r = − .56 p < .001), and between fulfillment and interpersonal disengagement (r = − .51 p < .001), and a significant, strong positive correlation between exhaustion and interpersonal disengagement (r = .68 p < .001). We performed Spearman correlation for the medical error scale. We found a marginally significant negative correlation between medical error and fulfillment (ρ = − .12 p = .060), a significant, positive, weak correlation between medical errors and exhaustion (ρ = .14 p = .030) and between medical errors and interpersonal disengagement (ρ = .26 p < .001). Validity analysis To test the validity of the questionnaire, we conducted Spearman correlation analyses between the dimensions of the questionnaire and the variables of work meaningfulness, multidimensional work motivation and life satisfaction (see Table 7 ). The SPFI fulfillment subscale showed significant positive correlation with all three subscales of WAMI and the MWMS’ introjected regulation subscale. The fulfillment subscale showed a strong significant correlation of the MWMS identified regulation subscale and showed an exceptionally strong significant association with MWMS intrinsic regulation subscale. In addition, fulfillment had a strong significant correlation with satisfaction with life. Concerning work motivation, a significant negative moderate relationship was found between fulfillment and amotivation subscale of MWMS. The SPFI exhaustion subscale had a significant positive relationship with MWMS’ extrinsic social motivation and amotivation subscales. The SPFI exhaustion subscale had a significant negative relationship with all three subscales of WAMI, with the identified regulation subscale of MWMS and with MWMS intrinsic regulation subscale. In addition, exhaustion showed a significant negative correlation with satisfaction with life. The SPFI interpersonal disengagement subscale had a significant positive correlation with medical error scale and with MWMS extrinsic social motivation. The SPFI interpersonal disengagement subscale had a significant negative relationship with all three subscales of WAMI and with MWMS identified and intrinsic regulation subscale. In addition, interpersonal disengagement showed a significant negative correlation with satisfaction with life. The SPFI medical error scale had significant negative weak correlation with WAMI “Positive Meaning” and “Greater Good” subscales. Table 7 SPFI-H’s validity analysis Scales Fulfillment Work Exhaustion Interpersonal Disengagement Medical Error ρ p Ρ p ρ p ρ p WAMI Positive Meaning .57 < .001 − .29 < .001 − .34 < .001 − .16 .009 WAMI Meaning Making .43 < .001 − .31 < .001 − .30 < .001 − .10 .102 WAMI Greater Good .40 < .001 − .22 < .001 − .29 < .001 − .13 .038 MWMS Amotivation − .42 < .001 .31 < .001 .37 < .001 .18 .003 MWMS Extr. Social. − .05 .466 .20 .001 .23 < .001 .14 .025 MWMS Extr. Material .01 .888 .04 .493 .11 .086 .05 .425 MWMS Introjected .13 .040 .02 .717 .02 .771 .03 .685 MWMS Identified .37 < .001 − .32 < .001 − .37 < .001 − .16 .012 MWMS Intrinsic .65 < .001 − .54 < .001 − .54 < .001 − .19 .002 SWLS .42 < .001 − .35 < .001 − .29 < .001 − .04 .487 Note . ρ indicates Spearman correlation coefficient. WAMI: Work and Meaning Inventory, MWMS: Multidimensional Work Motivation Scale, SWLS: Satisfaction with Life Scale. Association with demographic characteristics We examined whether there was a gender difference in SPFI and Medical Error scores. We performed normality test and test for homogeneity of variance (Levene test) comparison independent sample t-test with Cohen's d as effect size value, whereas medical error comparison was made by Mann-Whitney test with Rank-biserial correlation as effect size value. The results show no significant gender difference in either SPFI or medical error (Table 8 ). Table 8 Descriptive statistics of SPFI-H’s subscales demographic characteristics male female Subscales M SD M SD t / W p effect size Professional Fulfillment ¹ 11.55 3.06 11.11 3.26 1 0.31 0.13 Work Exhaustion ¹ 5.66 3.71 6.57 4.12 -1.67 0.09 -0.22 Interpersonal Disengagement¹ 7.63 5.97 7.23 6.24 0.47 0.63 0.06 Medical Error² 3.64 3.31 3 3.07 7718 0.12 0.11 Note. ¹ comparison was made by independent sample t-test with Cohen's d as effect size value. ² comparison was made by Mann-Whitney test with Rank-biserial correlation as effect size value . Spearman correlations were used to analyze the correlations of the SPFI subscales with age, place of residency, level of qualification, years spent working in healthcare, number of current positions and the dichotomous variables of type of healthcare provider. There was a significant negative relationship between exhaustion and age (ρ = − .16, p = .006), and between exhaustion and working outside Hungary (ρ = − .12, p = .046). Interpersonal disengagement showed a significant negative relationship with both age (ρ = − .18, p = .002) and career progression (ρ = − .14, p = .020). There also was a significant negative association between the number of self-reported medical errors and age (ρ = − .12, p = .041). Hierarchical multiple logistic regression analyses To explore factors that contribute to the experience of fulfillment, we conducted hierarchical multiple regression. For this, a separate standard multiple linear regression (blockwise model) was conducted for each of the endogenous variables. Descriptive statistics for independent and dependent variables are presented in Table 9 . Tests for collinearity showed that there was neither collinearity nor multicollinearity between variables. The correlation between predictor variables was r = .647, and the tolerance values were high for all predictors: gender 98.3%, age 96%, level of qualification 90%, MWMS - extrinsic social 76.6%, MWMS - extrinsic material 82.07%, MWMS - intrinsic motivation 55.8%, WAMI - positive meaning 56.8%. The independent variables were divided into three blocks based on their theoretical relevance. Model 1 included three demographic characteristics, with age, gender (1 = male, 2 = female), and level of qualification (1: intern, 2: resident, 3: fellow, 4: specialist, 5: specialist with more than one specialty) as the predictors, with levels of fulfillment as the dependent variable. Model 2 included MWMS - extrinsic social motivation, MWMS - extrinsic material motivation and MWMS - intrinsic motivation variables. Model 3 included positive meaning as a facet of work and meaning inventory. The results of multiple hierarchical logistic regression analysis using fulfillment as the dependent variable are shown in Tables 10 and 11 . In step one, age, gender, and level of qualification explain 1% of the variance in fulfillment; in this block, age, gender, and level of qualification made non-significant contributions to fulfillment. In step two, the MWMS predictors explain 51% of the variance in fulfillment. Additionally, with the inclusion of motivation factors into the model, a significant negative gender effect emerged. Based on results, fulfillment is significantly lower for women than for men. Our results show that extrinsic material motivation has a marginally significant positive effect. In contrast, no significant effect of extrinsic social motivation was found. Both the model and the explained variance showed a significant increase compared to the previous model. All three predictor variables significantly predict the level of fulfillment, and each has a positive effect on it. In step three, by including the WAMI positive meaning predictor, 59% of the variance in fulfillment is explained. The significant gender difference effect remains and along with it, a significant effect of the level of qualification appears. We found a significant negative effect between the level of qualification and fulfillment. However, by including positive meaning into the model, the relationship changed, and a significant positive effect was observed between level of qualification and positive meaning and between positive meaning and fulfillment. Therefore, we interpret positive meaning as a suppressor variable in the model. The final model showed that MWMS - extrinsic social motivation, MWMS - extrinsic material motivation, MWMS - intrinsic motivation and positive meaning as a facet of meaningfulness of work had a positive association with fulfillment; significantly predicting the level of fulfillment and each has a positive impact on it. Overall, the regression analysis highlighted the importance of various motivational factors and meaningfulness in explaining fulfillment. The model accounted for a substantial portion of the variance in fulfillment. Table 9 Descriptive statistics of independent and dependent variables Variables N Mean SD SE fulfillment 256 11.250 3.207 0.200 gender 256 1.699 0.460 0.029 age 256 47.125 46.409 2.901 level of qualification 256 4.945 1.016 0.064 MWMS_extrinsic social 256 8.863 3.714 0.232 MWMS_extrinsic material 256 6.910 3.679 0.230 MWMS_intrinsic motivation 256 15.668 3.899 0.244 WAMI_positive meaning 256 17.297 2.729 0.171 Table 10 Modell summary of hierarchical linear regression Model R R² Adjusted R² RMSE R² Change F Change df1 df2 p M 1 0.113 0.012 0.001 3.204 0.012 1.096 3 252 0.351 M 2 0.715 0.511 0.5 2.267 0.498 84.798 3 249 < .001 M 3 0.769 0.592 0.580 2.076 0.080 48.841 1 248 < .001 Table 11 Standardized and unstandardized coefficients with p-values of the final model Collinearity Statistics Model B SE (B) β t p Tolerance VIF M 1 Constans 12.240 1.190 10.286 < .001 gender -0.446 0.439 -0.064 -1.017 0.310 0.989 1.011 age 0.007 0.004 0.093 1.473 0.142 0.974 1.027 level of qualification -0.108 0.201 -0.034 -0.539 0.590 0.967 1.035 M 2 Constans 3.239 1.171 2.767 0.006 gender -0.785 0.311 -0.113 -2.520 0.012 0.985 1.016 age 0.004 0.003 0.052 1.147 0.252 0.962 1.039 level of qualification -0.194 0.146 -0.062 -1.331 0.184 0.919 1.088 MWMS_extr.peer 0.035 0.044 0.041 0.805 0.422 0.768 1.302 MWMS_extr.material 0.074 0.043 0.085 1.740 0.083 0.821 1.218 MWMS_intrinsic 0.594 0.037 0.723 15.882 < .001 0.947 1.056 M 3 Constans -0.402 1.192 -0.337 0.736 gender -0.844 0.285 -0.121 -2.959 0.003 0.984 1.017 age 0.003 0.003 0.041 0.990 0.323 0.961 1.041 level of qualification -0.311 0.135 -0.099 -2.312 0.022 0.905 1.105 MWMS_extr.peer 0.021 0.040 0.024 0.516 0.607 0.766 1.305 MWMS_extr.material 0.071 0.039 0.081 1.807 0.072 0.821 1.218 MWMS_intrinsic 0.394 0.045 0.480 8.831 < .001 0.558 1.792 WAMI_PositiveMeaning 0.442 0.063 0.376 6.989 < .001 0.568 1.761 Note : Dependent variable: fulfillment Modell1: R 2 = .012 R 2 Adj = .001 f(3, 252) = 1.096 p = .351 Modell2: R 2 = .511 R 2 Adj = .5 f(6, 249) = 43.494 p < .001 Δ: F(3,249) = 84.798 p < .001 Modell3: R 2 = .592 R 2 Adj = .580 f(7, 248) = 51.420 p < .001 Δ: F(1,248) = 48.841 p < .001 DISCUSSION The purpose of this paper was to explore the construction of fulfillment in the context of practicing medicine. To achieve this, we presented a study conducted in Hungary that applies the SPFI to a sample of the Hungarian physician population. Thereafter, using the results of the questionnaire validation, we performed hierarchical multiple regression to discover the relationship between different predictors and the experience of fulfillment. To our knowledge, this is the first study to examine the psychometric properties of the SPFI in Hungary and explore the experience of fulfillment. The validation study for the original tool was conducted in the United States with a sample of 256 physicians from a single academic site ( 12 ). The current study was conducted on a sample of the same size, albeit with a higher proportion of female respondents, a higher mean age, and more heterogeneity in medical practice specialties. Our findings supported a 14-item version with a 3-factor structure. Consistent with the results of previous research, the reliability of the questionnaire was found to be adequate ( 9 , 10 ). We point out the exclusion of items four ("I feel in control when dealing with difficult problems at work") and six ("I'm contributing professionally in the ways I value most") from our analysis. Both had relatively low factor loadings (0.46), and their omission improved the model fit. Item four's misfit may stem from its focus on competence, while other items address feelings, indicating that Hungarian physicians may differentiate between how they feel and how competent they perceive themselves. Item six's exclusion likely reflects Hungarian physicians' work conditions, where roles like patient care, teaching, research, and leadership are typically separate, making the question difficult to comprehend. In our study, we aimed to answer the question: What makes physicians thrive? In other words, what factors contribute to the development of fulfillment, and can the experience of fulfillment be predicted? Increasingly, international trends in studies of physician well-being are seeking to focus not only on problems and pathomechanisms (e.g. burnout research) but also on describing the resources and coping strategies that can help physicians stay healthy and balanced ( 21 ). Research in Hungary on this topic has almost exclusively focused on the factors behind burnout, with less attention given to what lies beyond burnout. Győrffy and colleagues noted that a potential direction for future research in the field of medical studies is the investigation of fulfillment, although such studies have not yet been conducted in Hungary ( 22 ). We performed a hierarchical regression analysis using various variables related to work motivation and meaningfulness. In selecting the predictors, we drew from Self-Determination Theory (SDT), which states that the motivations behind a given activity can be imagined as a continuum. At one end of this continuum lies extrinsic motivation, which is entirely externally driven, while at the other end is intrinsic motivation, which leads to autonomous, self-directed behavior. Practicing medicine includes several extrinsic motivating factors, such as the social status of physicians, prestige, financial benefits, practice ownership, professional autonomy. Intrinsic motivation, on the other hand, is defined as engaging in an activity for its own sake, because it is inherently interesting and enjoyable ( 23 ). Intrinsic motivating factors could be a sense of calling, perception of rewarding work, meaningful long term relationship with patients or personal past experience ( 24 ). Positive meaning is a straightforward reflection of the idea of psychological meaningfulness in terms of work psychology. The meaning of perception is ultimately shaped by the individual, though it is also affected by the surrounding environment or social context ( 25 ). This study found the significant importance of intrinsic motivation since we found that gender and educational status became significant when we included intrinsic motivation into the model. Healthcare professionals are often intrinsically motivated by a passion for helping others, the satisfaction of improving patient outcomes, and providing compassionate care. Personal fulfillment could come from mastering complex skills and making a difference, while their professional identity is deeply tied to their role as caregivers and problem-solvers. Autonomy in clinical decision-making is another key motivator, as it aligns with their values and investment in their work ( 26 ). To sum up, intrinsic motivation could help counter burnout by fostering resilience, providing a sense of purpose, and reducing stress. Our results confirm Ryan and Deci's theory of self-determination, i.e. autonomous, self-directed behavior contributes significantly to the development of fulfillment ( 27 ). We found that the inclusion of motivations led to a significant negative gender effect - in favour of men. To understand this, we conducted further regression analysis. We found that only intrinsic motivation showed a significant negative effect. Our findings support the research of Ádám and Győrffy, who found that due to the traditional family-centered nature of Hungarian society and the high employment rate of Hungarian women physicians, conflict between work and family roles may be more common among women physicians than their male colleagues. In Hungary, societal expectations force women to take on primarily family roles, while workplace expectations are also high, which can lead to increased stress and role conflict. Previous research has shown that conflict is closely linked to burnout syndrome, in particular to reduced performance. However, high job satisfaction can reduce this conflict ( 28 ). This could be a possible explanation for the higher fulfillment found among men. Our results showed that social motivation had no significant effect on the experience of fulfillment. The social aspect of extrinsic regulation in SDT highlights how social influences—such as expectations, recognition, and cultural norms—shape motivation ( 29 ). Our findings may reflect the organizational sociology of Hungarian health care institutions. Briefly, health care organizations differ from other organizations in a number of ways - these relate primarily to inequalities of status and prestige. Being a physician as a status and in terms of function occupies a specific place in the allocation of tasks and the hierarchical system of healthcare organizations. This results in involvement in the operation of the institution not only as a professional but also as a leader and as someone who gives guidance. The physician’s competence as a medical professional and as a leader is exclusive, unquestionable, and comprehensive. Such a presumption of omnipotence and omnicompetence is specific to healthcare institutions and, within them, to the medical profession alone ( 30 ). We assume that these factors may be behind the results obtained. Meaningful work and fulfillment are strongly interrelated experiences - there is a strong positive relationship between them. Our results support the research of Steger and colleagues who also found a high degree of association between meaningfulness and well-being. People who believe their work is important report better well-being, consider their work to be more central and important, they value their work more and report greater satisfaction at work. Those who feel their work is a calling, report greater satisfaction with work and for instance, spend more unpaid work hours at their own discretion ( 31 ). We are not aware of any previous research on this topic in Hungary, so our results should be considered novel ( 32 ). Positive meaning – related to meaningful work, reflects if people find their work to hold personal meaning, significance, or purpose ( 31 ). Our results suggest that positive meaning plays a suppressor role by enhancing the perceived relationship between level of qualification and fulfillment. As careers progress, physicians who find meaning or purpose in their work may experience higher levels of fulfillment. In the absence of finding meaning or purpose with the progress of time, a decreasing level of fulfillment is likely. It is assumed that this may be due to experiencing less immediate success or emotional exhaustion. This contradicts the findings of Győrffy and colleagues who found that the youngest medical age group (< 35) and residents are the most vulnerable to all aspects of burnout ( 33 ). However, our findings are in line with international research: the latest US Medical Association study on burnout found that the 45–55 age group stands out for its high rates of burnout ( 34 ). It is not yet known what processes might be behind this; further exploration is needed to achieve understanding. FUTURE DIRECTIONS AND RESEARCH LIMITATIONS Our study contributes to the understanding of professional fulfillment within the Hungarian physician community by using a rigorously validated scale. Future research could explore specific subgroups with different configurations in the SPFI dimensions and examine how these subgroups' well-being relates to their work conditions. It is important to note that the study sample was not representative, and the gender proportion was not balanced. Additionally, the cross-sectional design prevents us from making causal interpretations of the observed relationships. Moreover, we assessed the SPFI scales and medical errors through physicians' self-reports, which could introduce various biases, such as recall bias. For example, physicians experiencing burnout symptoms may be more likely to recall medical errors. CONCLUSION Work motivation among physicians has not yet been comprehensively studied, and the factors that play a role in the development of fulfillment are also considered a novelty in this field. To the best of our knowledge, in Hungary no research has been conducted that examines the work motivation and the experience of fulfillment among professionals working in the healthcare sector, particularly in medical positions. Previous studies have generally focused on work motivation in broader terms or among employees in other sectors; however, the unique characteristics of the medical profession and the conditions under which the work is performed require special attention. Therefore, our research presents a novelty in the field not only by exploring work motivation but also by investigating the experience of fulfillment and the relationships between these factors. The unique nature of the work performed by healthcare professionals, the stress, the high level of responsibility, and the constant commitment are all factors that play a significant role in shaping motivation and personal satisfaction. As such, our research offers not only a new scientific contribution but also practical applications that could help improve the work environment and workplace well-being in the healthcare sector. Abbreviations SPFI Stanford Professional Fulfillment Index SPFI-H Stanford Professional Fulfillment Index - Hungarian MOLBI Mini Oldenburg Burnout Questionnaire WAMI Work and Meaning Inventory SRH Subjective rated health status SWLS Satisfaction with Life Scale MWMS Multidimensional Work Motivation Scale CFA Confirmatory Factor Analysis MLR Maximum likelihood parameter estimator with robust standard error estimation HCW healthcare workers PSS Perceived Stress Scale MBI EE Maslach Burnout Inventory for Human Service Survey BDI Beck Depression Inventory Declarations Ethics approval and consent to participate All participants provided informed consent before taking part in the study. Prior to participation, they received detailed information regarding the study’s purpose, procedures, potential risks, and their right to withdraw at any time without consequences and that the data would be processed and published in the form of aggregated statistical results. Participants indicated their consent by acknowledgment in the initial part of the survey. The study was approved by the Scientific and Ethical Board of the Medical Research Council, the central board for medical research supervision affiliated with the Hungarian Ministry of Internal Affairs (https://ett.okfo.gov.hu/en/secretariat/; approval nr. 2023-03). The investigation conforms to the principles outlined in the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare no competing interests. Funding The research was funded by the University of Szeged Open Access Fund. Grant ID: 7443 The research was partly supported by the NKFIH project no. 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Assessing the mental health of Hungarian physicians with the Stanford Professional Fulfillment Index","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePhysicians\u0026rsquo; well-being has an emerging focus in recent research, whereas the medical community increasingly acknowledges its pivotal role in physicians\u0026rsquo; job performance and health, workplace climate and physician-patient relationships (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Physicians' wellness (used interchangeably with physician well-being) is a complex construct that includes physicians' physical, mental, and emotional health. Fulfillment is a more intrinsic, deeper sense of purpose and satisfaction in life which contributes to well-being. Although increasing importance is being attached to physicians\u0026rsquo; well-being, little effort has been made to define the construct, resulting in a lack of agreement on how physician well-being should be understood and assessed (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this paper, our goal is to understand what factors affect the professional fulfillment of Hungarian physicians. Accordingly, in the first part, we present a validation study regarding physicians\u0026rsquo; well-being assessment performed with Hungarian physicians using the \u003cem\u003eStanford Professional Fulfillment Index.\u003c/em\u003e In the second part, we performed hierarchical multiple regression with the SPFI-H to explore the relationship between potential influencing factors and fulfillment.\u003c/p\u003e\n\u003ch3\u003ePhysicians’ well-being\u003c/h3\u003e\n\u003cp\u003eWell-being itself cannot be easily defined, which is well shown by the fact that Brady and colleagues were able to collect more than ten different explicit physician well-being definitions (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Most definitions describe the following aspects of physician well-being: \u003cem\u003eemotional health, physical health, feeling challenged, spiritual well-being, work-life balance, thriving, authentic expressiveness and vigor.\u003c/em\u003e In contrast, the negative aspect of physician's well-being \u0026ndash; unwellness - describes conditions that make it difficult to concentrate on work and that can ultimately lead to burnout. Such is \u003cem\u003eemotional exhaustion fatigue\u003c/em\u003e, \u003cem\u003eanxiety, depression\u003c/em\u003e, or \u003cem\u003eamotivation.\u003c/em\u003e Furthermore, lesser-known factors are important in terms of both wellness and unwellness, thus can be considered ambivalent. Such as: \u003cem\u003emotivation to work, work as meaning, emotional vulnerability\u003c/em\u003e or \u003cem\u003eexperiencing embeddedness in the hierarchical system of healthcare institutions.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Hungarian context: Physicians\u0026rsquo; well-being in Hungary\u003c/h2\u003e \u003cp\u003eIn 2022, Sp\u0026aacute;nyik et al. performed a countrywide cross-sectional survey among healthcare workers (HCW), focusing on the mental health status (including depression and burnout syndrome) of HCWs and its possible predictors related to the COVID-19 outbreak (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). They assessed the mental health status related factors by Perceived Stress Scale (PSS). Work-related stress was assessed using the Professional Quality of Life Scale. Emotional exhaustion factor of burnout was assessed using the Emotional Exhaustion subscale of Maslach Burnout Inventory for Human Service Survey (MBI EE). Depression was assessed using the shortened (9 items) Hungarian version of the Beck Depression Inventory (BDI). According to their findings, both perceived and work-related stress exacerbated burnout. There was no direct relationship observed between COVID-19-related objective factors (e.g., working on the front lines or having to work in another department during the pandemic) and stress, burnout, or depression. Their findings suggest that the pandemic's challenges had only a mediated effect because stressors exert their influence solely through subjective perception. This underlines the significance of mental health assistance among healthcare workers.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevious studies with SPFI\u003c/h3\u003e\n\u003cp\u003eSince its publication in 2018, several studies have used the SPFI to assess burnout and fulfillment in physicians. For instance, Vetter and colleagues tested the SPFI as a stand-alone instrument to assess different aspects of physicians\u0026rsquo; well-being. They found all SPFI subscales significantly correlated with all BPI subscales and both single-item Burnout instruments. Self-reported medical errors had a low correlation with the SPFI interpersonal disengagement subscale and did not correlate with work exhaustion and professional fulfillment subscale. This indicates that SPFI, a simple 16-item questionnaire, could be a single instrument of assessing the well-being of physicians, moreover the measuring could be replicable (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecently, Lu and colleagues conducted a cross-sectional survey of 771 Society for Academic Emergency Medicine (SAEM) members. They added two new items to the interpersonal disengagement subscale (\"Less empathetic with trainees\" and \"More likely to be impatient or terse with clinical support staff\") which account for additional aspects of depersonalization that may be prevalent in academic emergency medicine. Cronbach's alpha for each of the nine domains ranged from 0.77 to 0.91(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEckstein and colleagues examined how burnout is related to mindfulness, fulfillment, specialty choice, and other lifestyle factors in a multidisciplinary population, including radiation oncology. They performed a survey with three sections (mindfulness assessment, personal and lifestyle factors hypothesized to be related to fulfillment and SPFI). They found that mindfulness positively correlated with fulfillment, negatively correlated with exhaustion and interpersonal disengagement. Overall, their results confirmed that SPFI is an easy-to-use, quick-to-complete questionnaire, of which the reliability and validity have been demonstrated in previous studies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLanguage adaptations\u003c/h3\u003e\n\u003cp\u003eTo this day, SPFI was adapted only in two languages. The Brazilian version was adapted to a sample of 432 physicians (n\u0026thinsp;=\u0026thinsp;432) who performed activities in the field of occupational medicine. Silva et al performed exploratory and confirmatory factor analysis. Reliability and validity indicators were good in their sample (Cronbach\u0026rsquo;s alpha (.95), McDonald\u0026rsquo;s omega (.95), and ORION (greatest lower bound) (.98) and the three-factor structure found by the original authors was replicated (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Japanese version, (n\u0026thinsp;=\u0026thinsp;666) also found a three-factor structure and proved to be reliable and valid. Cronbach's alphas of PFI subscales was 0.91 in professional fulfillment, 0.80 in burnout: work exhaustion, 0.90 in burnout: interpersonal disengagement, and 0.89 in burnout (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAims of the study\u003c/h3\u003e\n\u003cp\u003eThe aim of our study was to bring the mental health factors of physician\u0026rsquo;s well-being (general and characteristic of Hungary) into focus. In order to better understand the complex structure of physicians\u0026rsquo; well-being, we are introducing the SPFI-H: an instrument to measure physician\u0026rsquo;s well-being. We present a study performed in Hungary which applies the SPFI on a sample of the Hungarian physician population. Moreover, we performed hierarchical multiple regression with the SPFI-H to explore the relationship between different predictors and the experience of fulfillment. In the following, we outline the psychometric characteristics of our study.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eThe current study was a cross-sectional survey, the sample consisted of physicians who have at least a degree in medicine and are currently pursuing medical practice. The recruitment period started on 18 January 2023, ended on 14 June 2023. The participants were reached by means of an online questionnaire. The questionnaire study was created using Lime Survey. Using purposive sampling, we sent a link of the questionnaire directly to individuals who met the criteria and asked them to share it further among medical professional groups (e.g. medical Facebook groups such as Hungarian Medical Chamber). All questions were obligatory; thus, we excluded partial answers.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e \u003cp\u003e All participants provided informed consent before taking part in the study. Prior to participation, they received detailed information regarding the study\u0026rsquo;s purpose, procedures, potential risks, and their right to withdraw at any time without consequences and that the data would be processed and published in the form of aggregated statistical results. Participants indicated their consent by acknowledgment in the initial part of the survey, in compliance with ethical guidelines outlined by the Ethical Board of the Hungarian Council of Health Sciences (nr. 2023-03). The investigation conforms to the principles outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample\u003c/h3\u003e\n\u003cp\u003eBased on the guideline, confirmatory factor analysis requires a minimum of 10\u0026ndash;15 respondents per item, therefore, for current research, the required size is 240 individuals (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A total of 458 participants opened the questionnaire and 256 participants completed it. The sample included 179 females (69.1%) and 77 males (30.8%). The mean age of the participants was 44.3 years (SD\u0026thinsp;=\u0026thinsp;12.6, min\u0026thinsp;=\u0026thinsp;24, max\u0026thinsp;=\u0026thinsp;78). We asked the participants about their current place of residence, their marital status, their level of qualifications, their work experience in healthcare, the type of healthcare institution in which they work and the current number of positions they hold as a medical professional (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSample characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecapital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (44.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecity with county rights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (29.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (18.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evillage/municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLevel of qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eintern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eresident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (7.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003especialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (48.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003especialist with more than 1 specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (30.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (16.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ein a relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (25.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (56.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eother/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYears spent working in healthcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (70.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (15.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emore, than 21 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of current positions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (44.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (33.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (17.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emore, than three\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (5.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eType of healthcare provider (multiple choice possible)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (56.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (44.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprivate healthcare provider\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (23.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003especialist outpatient clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoutside of Hungary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (17.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGiven that the inclusion criteria were having at least a degree in medicine and currently pursuing medical practice, we also asked about the type of specialty(ies). The sample\u0026rsquo;s detailed distribution of the specialties is given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency data of type of specialty of the sample.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of specialty (multiple choice possible)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaediatrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (14.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI do not have a specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupational medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool health and youth protection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychiatry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastroenterology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetrics and Gynaecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar, nose and throat medicine; Surgery; Urology; Allergology and clinical immunology; Endocrinology and metabolic diseases; Nephrology; Sports medicine; Anaesthesiology and intensive care; Andrology; Addictology; Maxillo-facial and oral surgery; Haematology; Disaster medicine; Orthopaedics and traumatology; Medical rehabilitation; Audiology; Dermatology; Paediatric surgery; Forensic medicine; Infectiology; Hand surgery; Clinical neurophysiology; Clinical pharmacology; Preventive medicine and public health; Public health and hygiene; Pathology; Rehabilitation medicine; Rheumatology; Ophthalmology; Transfusiology; Pulmonary medicine;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEach of these categories had n\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;4 (1.2%) respondents indicating them.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eThe study used \u003cem\u003eStanford Professional Fulfillment Index (SPFI).\u003c/em\u003e SPFI contains 16\u0026thinsp;+\u0026thinsp;4 items assessing experiences of professional fulfillment over the previous 2 weeks and it is divided into two main sections measuring the Professional Fulfillment Index and the Self-reported Medical Errors constructs. The Professional Fulfillment Index contains three subscales, including (a) professional fulfillment (six items; e.g., \u0026ldquo;I feel happy at work\u0026rdquo;), (b) job exhaustion (four items; e.g., \u0026ldquo;During the past two weeks I have felt a sense of dread when I think about work, I have to do\u0026rdquo;), and (c) interpersonal disengagement (six items; e.g., \u0026ldquo;During the past two weeks, my job has contributed to me feeling less empathetic with my patients\u0026rdquo;). The questionnaire does not contain reverse items and items are rated on a five-point Likert scale (0\u0026thinsp;=\u0026thinsp;not at all, 4\u0026thinsp;=\u0026thinsp;completely true). The scale is scored by summing the responses to the items, with a higher score indicating higher experience concerning the respective constructions. In the Self-reported Medical Errors section (four items), the respondents rated the recent occurrence of specific medical errors statements (e.g., \u0026ldquo;I made a major medical error that could have resulted in patient harm\u0026rdquo;) in their practice on a six-point Likert scale (0\u0026thinsp;=\u0026thinsp;never, 5\u0026thinsp;=\u0026thinsp;in the last week). Trockel et al. calculated Cronbach alpha of each PFI scale to estimate internal consistency reliability. Internal and test-retest reliability was good, for professional fulfillment, Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.91, r\u0026thinsp;=\u0026thinsp;.82; for work exhaustion Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.86, r\u0026thinsp;=\u0026thinsp;.71; for interpersonal disengagement Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.92, r\u0026thinsp;=\u0026thinsp;.80. The internal consistency reliability estimate for the self-reported medical error scale was α\u0026thinsp;=\u0026thinsp;.62 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eMini Oldenburg Burnout Questionnaire \u0026ndash; MOLBI.\u003c/em\u003e Based on the Oldenburg Burnout Questionnaire (OLBI), a shortened version Mini Oldenburg Burnout Questionnaire (MOLBI) was published in Hungarian to assess the symptoms of burnout. The questionnaire consists of 10 items and has two subscales, exhaustion and disengagement. Five items belong to each scale, and two of them are reversed. Responses are scored on a four-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly agree, 4\u0026thinsp;=\u0026thinsp;strongly disagree), with higher scores indicating higher levels of burnout. Reliability indicators for the questionnaire used in the current study: for exhaustion Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.51, for disengagement Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.67 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eWork and Meaning Inventory \u0026ndash; WAMI.\u003c/em\u003e The questionnaire was developed by Steger et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The original version measures job meaningfulness through three dimensions: \u0026ldquo;Positive Meaning\u0026rdquo;, \u0026ldquo;Meaning Making through Work\u0026rdquo;, and \u0026ldquo;Greater Good Motivations\u0026rdquo;. The questionnaire consists of 10 items rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;absolutely untrue, 5\u0026thinsp;=\u0026thinsp;absolutely true). The questionnaire proved valid and reliable for both the original and the Hungarian versions (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Reliability indicators for the questionnaire used in the current study are Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.82, .73, and .72, respectively.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSubjective rated health status - SRH\u003c/em\u003e. We measured the respondent's evaluation of their health status with the question \"Overall, how would you rate your health?\" The five response options ranged from very poor to excellent (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eSatisfaction with Life Scale - SWLS\u003c/em\u003e. The SWLS is a five-item questionnaire measuring a general evaluation of one\u0026rsquo;s life (e.g., \u0026ldquo;If I could live my life over, I would change almost nothing\u0026rdquo;). Respondents can indicate their level of agreement with statements on a seven-point scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree). The scale score is the sum of the responses to the items, with a higher score indicating higher life satisfaction (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The questionnaire proved valid and reliable for both the original and the Hungarian versions (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Reliability indicator for the questionnaire used in the current study is Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.88\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eMultidimensional Work Motivation Scale - MWMS\u003c/em\u003e. We used the validated Hungarian version of MWMS (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). When completing the questionnaire, respondents were asked to rate the extent to which each statement describes them on a seven-point scale (1\u0026thinsp;=\u0026thinsp;not at all; 7\u0026thinsp;=\u0026thinsp;completely). The question asks why the respondent makes an effort and puts energy into their work (i.e. what motivates them). The instrument contains six subscales such as, Amotivation, Extrinsic regulation - social, Extrinsic regulation \u0026ndash; material, Introjected regulation, Identified regulation, and Intrinsic regulation. Each subscale has 3 items, except for Introjected regulation, which has 4 items. Higher scores on a specific subscale indicate stronger work motivation of the kind. Reliability indicators for the questionnaire used in the current study are Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.78, .74, .83, .74, .83, and .90, respectively.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eDuring the validation process, the first step was to translate the original questionnaire to Hungarian by two independent professionals who were experts in the field (the English Hungarian translated version see in appendix). The translations were then discussed and used to create a consensual version for back-translation. Back-translation was made by a native American English speaker, bilingual (English - Hungarian) physician. Expert inspection of the back-translated text confirmed that it conveyed the same meaning as the original.\u003c/p\u003e \u003cp\u003eDuring the data analysis, we conducted a descriptive analysis, looking at frequencies, percentage distributions, means and standard deviations. After reporting the demographics, we carried out a confirmatory factor analysis (CFA) to test the model fit of the Professional Fulfillment Index section of the SPFI questionnaire. According to the original model, we defined three latent variables: professional fulfillment, work exhaustion, and interpersonal disengagement. Maximum likelihood parameter estimator with robust standard error estimation (MLR) was used for the CFA as we assumed that data were not normally distributed. The model fit assessment was based on the fit indices with the respective cut-off values: chi-square (χ\u0026sup2;) (p-value\u0026thinsp;\u0026gt;\u0026thinsp;.05), normed χ\u0026sup2;/df\u0026thinsp;\u0026lt;\u0026thinsp;3.0, comparative fit index (CFI) and Tucker\u0026ndash;Lewis fit index (TLI) of \u0026ge;\u0026thinsp;.95 (good) or \u0026ge;\u0026thinsp;.90 (acceptable), root mean square error of approximation (RMSEA) of \u0026le;\u0026thinsp;.08 and standardized root mean square residual (SRMR) of \u0026le;\u0026thinsp;.08 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Since the fit of the original model did not reach an acceptable level, we analyzed modification indices to remove potential items with cross-loadings and to allow reasonable residual covariances between items loading to the same factor. Finally, we checked the explained variances and factor loadings of the items, and we excluded items with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;25% and λ\u0026thinsp;\u0026lt;\u0026thinsp;.50.\u003c/p\u003e \u003cp\u003eAn additional CFA was carried out to test the internal structure of the Self-reported Medical Errors section of the SPFI questionnaire.\u003c/p\u003e \u003cp\u003eThe reliability was tested using Cronbach\u0026rsquo;s alpha. The suggested cut-off values for Cronbach\u0026rsquo;s alpha were excellent (0.90 and above), high (0.70\u0026ndash;0.90), moderate (0.50\u0026ndash;0.70), and low (0.50 and below).\u003c/p\u003e \u003cp\u003eThe scale scores were calculated by summing up the values of the corresponding items. We reported the descriptive statistics of the scales (mean, standard deviation, minimum, maximum). Normality of the subscales was measured using skewness and kurtosis values.\u003c/p\u003e \u003cp\u003eBased on the results of normality testing, Pearson or Spearman correlation was used to examine the correlations between the SPFI subscales and the associations with WAMI, MWMS and SWLS. Independent sample t-test or Mann-Whitney U-test were used to determine gender differences. Spearman correlations were used to measure the association with age, place of residency, level of qualification, years spent working in healthcare, number of current positions and the dichotomous variables of type of healthcare provider.\u003c/p\u003e \u003cp\u003eWe performed a hierarchical multiple regression to predict fulfillment based on seven independent variables: gender, age, level of qualification, different facets of work motivations and positive meaning in work. Predictors were included in the model based on prior research and theoretical relevance.\u003c/p\u003e \u003cp\u003eThe model fit was assessed using R-squared and adjusted R-squared values, and the statistical significance of individual predictors was evaluated with p-values. All assumptions of multiple linear regression, including linearity, homoscedasticity, and normality of residuals, were assessed through diagnostic plots. Multicollinearity was checked using variance inflation factors (VIFs). The regression model is specified as follows: Fulfillment\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003e (MWMS extrinsic social) +β\u003csub\u003e2\u003c/sub\u003e (MWMS extrinsic material) + β\u003csub\u003e3\u003c/sub\u003e (MWMS intrinsic motivation) + β\u003csub\u003e4\u003c/sub\u003e (WAMI positive meaning). All analyses were performed using JASP 0.19.1; (JASP Team, 2024).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eConfirmatory Factor Analysis (CFA) results for SPFI-H questionnaire\u003c/h2\u003e\n \u003cp\u003eThe CFA showed that the model of the original 16 item Professional Fulfillment Index had a poor fit (Model 1 in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The model\u0026rsquo;s misfit was improved by considering the residual covariances justified to theoretical and methodological reasons. Based on the modification indices, we performed a stepwise modification process to achieve adequate fit while preserving the model\u0026rsquo;s theoretical integrity. In Model 2 and 3, we included residual covariances between the items of Interpersonal Disengagement 2 and 6 (\u0026ldquo;\u003cem\u003eDuring the past two weeks my job has contributed to me feeling less empathetic with my colleagues\u0026rdquo;\u003c/em\u003e and \u0026ldquo;\u003cem\u003eDuring the past two weeks my job has contributed to me feeling less connected with my colleagues\u003c/em\u003e\u0026rdquo;) and between the items of Interpersonal Disengagement 4 and 5 (\u0026ldquo;\u003cem\u003eDuring the past two weeks my job has contributed to me feeling less interested in talking with my patients\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003eDuring the past two weeks my job has contributed to me feeling less connected with my patients\u003c/em\u003e\u0026rdquo;) (Model 2 and 3, respectively). We also omitted the items of Professional Fulfillment 6 and 4 from the model (\u0026ldquo;\u003cem\u003eI\u0026rsquo;m contributing professionally (e.g. patient care, teaching, research, and leadership) in the ways I value most\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003eI feel in control when dealing with difficult problems at work\u003c/em\u003e\u0026rdquo;), because the explained variance and the factor loadings of these items were low (R\u0026sup2; = .22 and .23 and \u0026lambda;\u0026thinsp;=\u0026thinsp;.46 and .46 respectively) (Model 4 and 5, respectively).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of fit indices for confirmatory factor analysis of Hungarian adaptation of Stanford Professional Fulfillment Index (SPFI-H)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;/df\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI of RMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e461.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.11-.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08-.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e304.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08-.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08-.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e222.38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.94\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.94\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.07-.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e Model 1: original factor structure; Model 2: Residual covariance between Disengagement 2 and Disengagement 6 introduced; Model3: Residual covariance between Disengagement 4 and Disengagement 5 introduced; Model4: Fulfillment 6 removed; Model5: Fulfillment 4 removed, FINAL MODEL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe final model showed adequate fit indices (Model 5 in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), and all the items had acceptably high factor loadings on their respective constructs (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). See Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for visual representation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactor loadings of Stanford Professional Fulfillment Index\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lambda;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eConfidence interval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eProfessional Fulfillment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFulfill 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFulfill 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFulfill 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFulfill 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eWork Exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorkexh 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorkexh 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorkexh 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorkexh 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eInterpersonal Disengagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiseng 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiseng 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiseng 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiseng 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiseng 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiseng 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAn additional CFA was performed to assess the structural validity of the Self-reported Medical Errors scale. Relative fit indices indicated good fit (CFI\u0026thinsp;=\u0026thinsp;.91 and IFI\u0026thinsp;=\u0026thinsp;.91) but chi-squared test and RMSEA indicated a poor fit (\u0026chi;\u0026sup2; (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;25.69 p\u0026thinsp;\u0026lt;\u0026thinsp;.001, RMSEA\u0026thinsp;=\u0026thinsp;.21 [CI\u0026thinsp;=\u0026thinsp;.15-.29]. The explained variances and factor loadings of all four items were acceptable (R\u0026sup2; values were between .30 and 62 and \u0026lambda; values were between .52 and .84). Therefore, we decided to retain the subscale.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eReliability analysis\u003c/h2\u003e\n \u003cp\u003eReliability was assessed using Cronbach\u0026apos;s alpha values. The coefficients obtained indicated good to excellent reliability of the subscale. The Cronbach\u0026apos;s alpha for the professional fulfillment subscale of the SPFI-H questionnaire was .83, for work exhaustion was .83, for interpersonal disengagement was .91, and for the Self-reported Medical Error scale was 0.74.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive statistics of the subscales\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics of the participants\u0026apos; SPFI scores are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. In terms of skewness and kurtosis, professional fulfillment, work exhaustion and interpersonal disengagement subscales follow approximately normal distributions. In contrast, for medical errors, there was a significant positive skew, i.e. a significant proportion of participants reported low levels of error in their work.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of the SPFI-H subscales\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubscales\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkew\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurt\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional Fulfillment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork Exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterpersonal Disengagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-reported Medical Errors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eUsing Pearson correlations, we found a significant, strong negative association between fulfillment and exhaustion (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.56 p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and between fulfillment and interpersonal disengagement (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.51 p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and a significant, strong positive correlation between exhaustion and interpersonal disengagement (r\u0026thinsp;=\u0026thinsp;.68 p\u0026thinsp;\u0026lt;\u0026thinsp;.001). We performed Spearman correlation for the medical error scale. We found a marginally significant negative correlation between medical error and fulfillment (\u0026rho; = \u0026minus;\u0026thinsp;.12 p\u0026thinsp;=\u0026thinsp;.060), a significant, positive, weak correlation between medical errors and exhaustion (\u0026rho;\u0026thinsp;=\u0026thinsp;.14 p\u0026thinsp;=\u0026thinsp;.030) and between medical errors and interpersonal disengagement (\u0026rho;\u0026thinsp;=\u0026thinsp;.26 p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eValidity analysis\u003c/h2\u003e\n \u003cp\u003eTo test the validity of the questionnaire, we conducted Spearman correlation analyses between the dimensions of the questionnaire and the variables of work meaningfulness, multidimensional work motivation and life satisfaction (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe SPFI fulfillment subscale showed significant positive correlation with all three subscales of WAMI and the MWMS\u0026rsquo; introjected regulation subscale. The fulfillment subscale showed a strong significant correlation of the MWMS identified regulation subscale and showed an exceptionally strong significant association with MWMS intrinsic regulation subscale. In addition, fulfillment had a strong significant correlation with satisfaction with life. Concerning work motivation, a significant negative moderate relationship was found between fulfillment and amotivation subscale of MWMS.\u003c/p\u003e\n \u003cp\u003eThe SPFI exhaustion subscale had a significant positive relationship with MWMS\u0026rsquo; extrinsic social motivation and amotivation subscales. The SPFI exhaustion subscale had a significant negative relationship with all three subscales of WAMI, with the identified regulation subscale of MWMS and with MWMS intrinsic regulation subscale. In addition, exhaustion showed a significant negative correlation with satisfaction with life.\u003c/p\u003e\n \u003cp\u003eThe SPFI interpersonal disengagement subscale had a significant positive correlation with medical error scale and with MWMS extrinsic social motivation. The SPFI interpersonal disengagement subscale had a significant negative relationship with all three subscales of WAMI and with MWMS identified and intrinsic regulation subscale. In addition, interpersonal disengagement showed a significant negative correlation with satisfaction with life.\u003c/p\u003e\n \u003cp\u003eThe SPFI medical error scale had significant negative weak correlation with WAMI \u0026ldquo;Positive Meaning\u0026rdquo; and \u0026ldquo;Greater Good\u0026rdquo; subscales.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" style=\"width: 643px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSPFI-H\u0026rsquo;s validity analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 128px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eScales\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 181.66px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eFulfillment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 131.933px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWork Exhaustion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 281.597px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eInterpersonal Disengagement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 94.926px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMedical Error\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eWAMI Positive Meaning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eWAMI Meaning Making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eWAMI Greater Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS Amotivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS Extr. Social.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS Extr. Material\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS Introjected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS Identified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS Intrinsic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\" align=\"left\"\u003e\n \u003cp\u003eSWLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125.66px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 158.854px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 205.602px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\" align=\"left\"\u003e\n \u003cp\u003e.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 818.116px;\" colspan=\"12\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e. \u003cem\u003e\u0026rho; indicates Spearman correlation coefficient.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eWAMI: Work and Meaning Inventory, MWMS: Multidimensional Work Motivation Scale, SWLS: Satisfaction with Life Scale.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation with demographic characteristics\u003c/h2\u003e\n \u003cp\u003eWe examined whether there was a gender difference in SPFI and Medical Error scores. We performed normality test and test for homogeneity of variance (Levene test) comparison independent sample t-test with Cohen\u0026apos;s d as effect size value, whereas medical error comparison was made by Mann-Whitney test with Rank-biserial correlation as effect size value. The results show no significant gender difference in either SPFI or medical error (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of SPFI-H\u0026rsquo;s subscales demographic characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubscales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003et / W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eeffect size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional Fulfillment \u0026sup1;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork Exhaustion \u0026sup1;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterpersonal Disengagement\u0026sup1;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical Error\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNote. \u0026sup1; comparison was made by independent sample t-test with Cohen\u0026apos;s d as effect size value. \u0026sup2; comparison was made by Mann-Whitney test with Rank-biserial correlation as effect size value\u003c/em\u003e.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSpearman correlations were used to analyze the correlations of the SPFI subscales with age, place of residency, level of qualification, years spent working in healthcare, number of current positions and the dichotomous variables of type of healthcare provider. There was a significant negative relationship between exhaustion and age (\u0026rho; = \u0026minus;\u0026thinsp;.16, p\u0026thinsp;=\u0026thinsp;.006), and between exhaustion and working outside Hungary (\u0026rho; = \u0026minus;\u0026thinsp;.12, p\u0026thinsp;=\u0026thinsp;.046). Interpersonal disengagement showed a significant negative relationship with both age (\u0026rho; = \u0026minus;\u0026thinsp;.18, p\u0026thinsp;=\u0026thinsp;.002) and career progression (\u0026rho; = \u0026minus;\u0026thinsp;.14, p\u0026thinsp;=\u0026thinsp;.020). There also was a significant negative association between the number of self-reported medical errors and age (\u0026rho; = \u0026minus;\u0026thinsp;.12, p\u0026thinsp;=\u0026thinsp;.041).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eHierarchical multiple logistic regression analyses\u003c/h2\u003e\n \u003cp\u003eTo explore factors that contribute to the experience of fulfillment, we conducted hierarchical multiple regression. For this, a separate standard multiple linear regression (blockwise model) was conducted for each of the endogenous variables. Descriptive statistics for independent and dependent variables are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTests for collinearity showed that there was neither collinearity nor multicollinearity between variables. The correlation between predictor variables was r\u0026thinsp;=\u0026thinsp;.647, and the tolerance values were high for all predictors: gender 98.3%, age 96%, level of qualification 90%, MWMS - extrinsic social 76.6%, MWMS - extrinsic material 82.07%, MWMS - intrinsic motivation 55.8%, WAMI - positive meaning 56.8%.\u003c/p\u003e\n \u003cp\u003eThe independent variables were divided into three blocks based on their theoretical relevance. Model 1 included three demographic characteristics, with age, gender (1\u0026thinsp;=\u0026thinsp;male, 2\u0026thinsp;=\u0026thinsp;female), and level of qualification (1: intern, 2: resident, 3: fellow, 4: specialist, 5: specialist with more than one specialty) as the predictors, with levels of fulfillment as the dependent variable. Model 2 included MWMS - extrinsic social motivation, MWMS - extrinsic material motivation and MWMS - intrinsic motivation variables. Model 3 included positive meaning as a facet of work and meaning inventory.\u003c/p\u003e\n \u003cp\u003eThe results of multiple hierarchical logistic regression analysis using fulfillment as the dependent variable are shown in Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e. In step one, age, gender, and level of qualification explain 1% of the variance in fulfillment; in this block, age, gender, and level of qualification made non-significant contributions to fulfillment.\u003c/p\u003e\n \u003cp\u003eIn step two, the MWMS predictors explain 51% of the variance in fulfillment. Additionally, with the inclusion of motivation factors into the model, a significant negative gender effect emerged. Based on results, fulfillment is significantly lower for women than for men. Our results show that extrinsic material motivation has a marginally significant positive effect. In contrast, no significant effect of extrinsic social motivation was found. Both the model and the explained variance showed a significant increase compared to the previous model. All three predictor variables significantly predict the level of fulfillment, and each has a positive effect on it.\u003c/p\u003e\n \u003cp\u003eIn step three, by including the WAMI positive meaning predictor, 59% of the variance in fulfillment is explained. The significant gender difference effect remains and along with it, a significant effect of the level of qualification appears. We found a significant negative effect between the level of qualification and fulfillment. However, by including positive meaning into the model, the relationship changed, and a significant positive effect was observed between level of qualification and positive meaning and between positive meaning and fulfillment. Therefore, we interpret positive meaning as a suppressor variable in the model.\u003c/p\u003e\n \u003cp\u003eThe final model showed that MWMS - extrinsic social motivation, MWMS - extrinsic material motivation, MWMS - intrinsic motivation and positive meaning as a facet of meaningfulness of work had a positive association with fulfillment; significantly predicting the level of fulfillment and each has a positive impact on it. Overall, the regression analysis highlighted the importance of various motivational factors and meaningfulness in explaining fulfillment. The model accounted for a substantial portion of the variance in fulfillment.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of independent and dependent variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efulfillment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elevel of qualification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWMS_extrinsic social\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWMS_extrinsic material\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMWMS_intrinsic motivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWAMI_positive meaning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModell summary of hierarchical linear regression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u0026sup2; Change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF Change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab10\" style=\"width: 539px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStandardized and unstandardized coefficients with p-values of the final model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 351px;\" colspan=\"7\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 212.062px;\" colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eCollinearity Statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 127px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003eSE (B)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003eM\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eConstans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e12.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e1.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e10.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e1.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003elevel of qualification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003eM\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eConstans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e3.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e1.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e2.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-2.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e1.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003elevel of qualification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-1.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS_extr.peer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS_extr.material\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e1.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS_intrinsic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e15.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003eM\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eConstans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-2.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003elevel of qualification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e-2.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS_extr.peer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS_extr.material\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e1.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eMWMS_intrinsic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e8.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 127px;\" align=\"left\"\u003e\n \u003cp\u003eWAMI_PositiveMeaning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\" align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\" align=\"left\"\u003e\n \u003cp\u003e6.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167.565px;\" colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44.4977px;\" align=\"left\"\u003e\n \u003cp\u003e1.761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 573.062px;\" colspan=\"11\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Dependent variable: fulfillment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 352.481px;\" colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eModell1: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.012 R\u003csup\u003e2\u003c/sup\u003e Adj\u0026thinsp;=\u0026thinsp;.001 f(3, 252)\u0026thinsp;=\u0026thinsp;1.096 p\u0026thinsp;=\u0026thinsp;.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"width: 141.581px;\" colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 573.062px;\" colspan=\"11\" align=\"left\"\u003e\n \u003cp\u003eModell2: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.511 R\u003csup\u003e2\u003c/sup\u003e Adj\u0026thinsp;=\u0026thinsp;.5 f(6, 249)\u0026thinsp;=\u0026thinsp;43.494 p\u0026thinsp;\u0026lt;\u0026thinsp;.001 \u0026Delta;: F(3,249)\u0026thinsp;=\u0026thinsp;84.798 p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 573.062px;\" colspan=\"11\" align=\"left\"\u003e\n \u003cp\u003eModell3: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.592 R\u003csup\u003e2\u003c/sup\u003e Adj\u0026thinsp;=\u0026thinsp;.580 f(7, 248)\u0026thinsp;=\u0026thinsp;51.420 p\u0026thinsp;\u0026lt;\u0026thinsp;.001 \u0026Delta;: F(1,248)\u0026thinsp;=\u0026thinsp;48.841 p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe purpose of this paper was to explore the construction of fulfillment in the context of practicing medicine. To achieve this, we presented a study conducted in Hungary that applies the SPFI to a sample of the Hungarian physician population. Thereafter, using the results of the questionnaire validation, we performed hierarchical multiple regression to discover the relationship between different predictors and the experience of fulfillment. To our knowledge, this is the first study to examine the psychometric properties of the SPFI in Hungary and explore the experience of fulfillment. The validation study for the original tool was conducted in the United States with a sample of 256 physicians from a single academic site (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The current study was conducted on a sample of the same size, albeit with a higher proportion of female respondents, a higher mean age, and more heterogeneity in medical practice specialties. Our findings supported a 14-item version with a 3-factor structure. Consistent with the results of previous research, the reliability of the questionnaire was found to be adequate (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe point out the exclusion of items four (\"I feel in control when dealing with difficult problems at work\") and six (\"I'm contributing professionally in the ways I value most\") from our analysis. Both had relatively low factor loadings (0.46), and their omission improved the model fit. Item four's misfit may stem from its focus on competence, while other items address feelings, indicating that Hungarian physicians may differentiate between how they feel and how competent they perceive themselves. Item six's exclusion likely reflects Hungarian physicians' work conditions, where roles like patient care, teaching, research, and leadership are typically separate, making the question difficult to comprehend.\u003c/p\u003e \u003cp\u003eIn our study, we aimed to answer the question: What makes physicians thrive? In other words, what factors contribute to the development of fulfillment, and can the experience of fulfillment be predicted? Increasingly, international trends in studies of physician well-being are seeking to focus not only on problems and pathomechanisms (e.g. burnout research) but also on describing the resources and coping strategies that can help physicians stay healthy and balanced (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch in Hungary on this topic has almost exclusively focused on the factors behind burnout, with less attention given to what lies beyond burnout. Győrffy and colleagues noted that a potential direction for future research in the field of medical studies is the investigation of fulfillment, although such studies have not yet been conducted in Hungary (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe performed a hierarchical regression analysis using various variables related to work motivation and meaningfulness. In selecting the predictors, we drew from Self-Determination Theory (SDT), which states that the motivations behind a given activity can be imagined as a continuum. At one end of this continuum lies extrinsic motivation, which is entirely externally driven, while at the other end is intrinsic motivation, which leads to autonomous, self-directed behavior. Practicing medicine includes several extrinsic motivating factors, such as the social status of physicians, prestige, financial benefits, practice ownership, professional autonomy. Intrinsic motivation, on the other hand, is defined as engaging in an activity for its own sake, because it is inherently interesting and enjoyable (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Intrinsic motivating factors could be a sense of calling, perception of rewarding work, meaningful long term relationship with patients or personal past experience (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Positive meaning is a straightforward reflection of the idea of psychological meaningfulness in terms of work psychology. The meaning of perception is ultimately shaped by the individual, though it is also affected by the surrounding environment or social context (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study found the significant importance of intrinsic motivation since we found that gender and educational status became significant when we included intrinsic motivation into the model. Healthcare professionals are often intrinsically motivated by a passion for helping others, the satisfaction of improving patient outcomes, and providing compassionate care. Personal fulfillment could come from mastering complex skills and making a difference, while their professional identity is deeply tied to their role as caregivers and problem-solvers. Autonomy in clinical decision-making is another key motivator, as it aligns with their values and investment in their work (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). To sum up, intrinsic motivation could help counter burnout by fostering resilience, providing a sense of purpose, and reducing stress. Our results confirm Ryan and Deci's theory of self-determination, i.e. autonomous, self-directed behavior contributes significantly to the development of fulfillment (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). We found that the inclusion of motivations led to a significant negative gender effect - in favour of men. To understand this, we conducted further regression analysis. We found that only intrinsic motivation showed a significant negative effect.\u003c/p\u003e \u003cp\u003eOur findings support the research of \u0026Aacute;d\u0026aacute;m and Győrffy, who found that due to the traditional family-centered nature of Hungarian society and the high employment rate of Hungarian women physicians, conflict between work and family roles may be more common among women physicians than their male colleagues. In Hungary, societal expectations force women to take on primarily family roles, while workplace expectations are also high, which can lead to increased stress and role conflict. Previous research has shown that conflict is closely linked to burnout syndrome, in particular to reduced performance. However, high job satisfaction can reduce this conflict (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This could be a possible explanation for the higher fulfillment found among men.\u003c/p\u003e \u003cp\u003eOur results showed that social motivation had no significant effect on the experience of fulfillment. The social aspect of extrinsic regulation in SDT highlights how social influences\u0026mdash;such as expectations, recognition, and cultural norms\u0026mdash;shape motivation (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Our findings may reflect the organizational sociology of Hungarian health care institutions. Briefly, health care organizations differ from other organizations in a number of ways - these relate primarily to inequalities of status and prestige. Being a physician as a status and in terms of function occupies a specific place in the allocation of tasks and the hierarchical system of healthcare organizations. This results in involvement in the operation of the institution not only as a professional but also as a leader and as someone who gives guidance. The physician\u0026rsquo;s competence as a medical professional and as a leader is exclusive, unquestionable, and comprehensive. Such a presumption of omnipotence and omnicompetence is specific to healthcare institutions and, within them, to the medical profession alone (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). We assume that these factors may be behind the results obtained.\u003c/p\u003e \u003cp\u003eMeaningful work and fulfillment are strongly interrelated experiences - there is a strong positive relationship between them. Our results support the research of Steger and colleagues who also found a high degree of association between meaningfulness and well-being. People who believe their work is important report better well-being, consider their work to be more central and important, they value their work more and report greater satisfaction at work. Those who feel their work is a calling, report greater satisfaction with work and for instance, spend more unpaid work hours at their own discretion (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). We are not aware of any previous research on this topic in Hungary, so our results should be considered novel (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePositive meaning \u0026ndash; related to meaningful work, reflects if people find their work to hold personal meaning, significance, or purpose (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our results suggest that positive meaning plays a suppressor role by enhancing the perceived relationship between level of qualification and fulfillment. As careers progress, physicians who find meaning or purpose in their work may experience higher levels of fulfillment. In the absence of finding meaning or purpose with the progress of time, a decreasing level of fulfillment is likely. It is assumed that this may be due to experiencing less immediate success or emotional exhaustion. This contradicts the findings of Győrffy and colleagues who found that the youngest medical age group (\u0026lt;\u0026thinsp;35) and residents are the most vulnerable to all aspects of burnout (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, our findings are in line with international research: the latest US Medical Association study on burnout found that the 45\u0026ndash;55 age group stands out for its high rates of burnout (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). It is not yet known what processes might be behind this; further exploration is needed to achieve understanding.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFUTURE DIRECTIONS AND RESEARCH LIMITATIONS\u003c/h2\u003e \u003cp\u003eOur study contributes to the understanding of professional fulfillment within the Hungarian physician community by using a rigorously validated scale. Future research could explore specific subgroups with different configurations in the SPFI dimensions and examine how these subgroups' well-being relates to their work conditions. It is important to note that the study sample was not representative, and the gender proportion was not balanced. Additionally, the cross-sectional design prevents us from making causal interpretations of the observed relationships. Moreover, we assessed the SPFI scales and medical errors through physicians' self-reports, which could introduce various biases, such as recall bias. For example, physicians experiencing burnout symptoms may be more likely to recall medical errors.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWork motivation among physicians has not yet been comprehensively studied, and the factors that play a role in the development of fulfillment are also considered a novelty in this field. To the best of our knowledge, in Hungary no research has been conducted that examines the work motivation and the experience of fulfillment among professionals working in the healthcare sector, particularly in medical positions. Previous studies have generally focused on work motivation in broader terms or among employees in other sectors; however, the unique characteristics of the medical profession and the conditions under which the work is performed require special attention. Therefore, our research presents a novelty in the field not only by exploring work motivation but also by investigating the experience of fulfillment and the relationships between these factors. The unique nature of the work performed by healthcare professionals, the stress, the high level of responsibility, and the constant commitment are all factors that play a significant role in shaping motivation and personal satisfaction. As such, our research offers not only a new scientific contribution but also practical applications that could help improve the work environment and workplace well-being in the healthcare sector.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStanford Professional Fulfillment Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPFI-H\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStanford Professional Fulfillment Index \u003cem\u003e-\u003c/em\u003e Hungarian\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMOLBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini Oldenburg Burnout Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWAMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWork and Meaning Inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubjective rated health status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSWLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSatisfaction with Life Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMWMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidimensional Work Motivation Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum likelihood parameter estimator with robust standard error estimation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehealthcare workers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePerceived Stress Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMBI EE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaslach Burnout Inventory for Human Service Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBeck Depression Inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent before taking part in the study. Prior to participation, they received detailed information regarding the study\u0026rsquo;s purpose, procedures, potential risks, and their right to withdraw at any time without consequences and that the data would be processed and published in the form of aggregated statistical results. Participants indicated their consent by acknowledgment in the initial part of the survey. The study was approved by the Scientific and Ethical Board of the Medical Research Council, the central board for medical research supervision affiliated with the Hungarian Ministry of Internal Affairs (https://ett.okfo.gov.hu/en/secretariat/; approval nr. 2023-03). The investigation conforms to the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was funded by the University of Szeged Open Access Fund. Grant ID: 7443\u003c/p\u003e\n\u003cp\u003eThe research was partly supported by the NKFIH project no. K 138372, which has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the K_21 funding scheme.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOGy, TM, VS, contributed to the conception and design of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOGy, TM, VS contributed to the development procedure of the Hungarian version of SPFI. OGy analysed the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026Aacute;.H. made the translation and back translation review, the whole paper English language proofreading, and contributed to the final drafting of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOGy wrote the first draft of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVS and MT supervised the analysis. MT, VS, revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Gergő Dr\u0026oacute;tos and Kl\u0026aacute;ra Solt\u0026eacute;sz-V\u0026aacute;rhelyi for their support and assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCho HL, Huang CJ. Why Mental Health\u0026ndash;Related Stigma Matters for Physician Wellbeing, Burnout, and Patient Care. J GEN INTERN MED [Internet]. 2020 May 1 [cited 2023 Aug 19];35(5):1579\u0026ndash;81. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medscape.com/slideshow/2024-lifestyle-burnout-6016865\u003c/span\u003e\u003cspan address=\"https://www.medscape.com/slideshow/2024-lifestyle-burnout-6016865\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"professional fulfillment, physician, well-being, burnout, validation, confirmatory factor analysis","lastPublishedDoi":"10.21203/rs.3.rs-6275614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6275614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and aims\u003c/strong\u003e: The mental health of physicians - and Hungarian physicians in particular - is a pressing issue and understanding the factors involved is of paramount importance. Professional fulfillment is a concept that looks at the mental health of physicians in a non-traditional way and has been studied little in this context. In Hungary, there was no instrument in the past that investigated the factors involved in the mental health of physicians, so in our research we focused on the exploration of fulfillment. We conducted a validation study with Hungarian physicians using the Hungarian version of the \u003cem\u003eStanford Professional Fulfillment Index (SPFI-H)\u003c/em\u003e. Moreover, we tested the potential influencing factors of professional fulfillment in relation to the physicians’ well-being and burnout.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e:\u0026nbsp;Physicians (n = 256) responded to an online questionary, containing SPFI-H, Mini Oldenburg Burnout Questionnaire – MOLBI, Work and Meaning Inventory – WAMI, Subjective rated health status – SRH, Satisfaction with Life Scale – SWLS and Multidimensional Work Motivation Scale - MWMS. The psychometric testing of the SPFI-H included internal consistency, convergent validity, discriminant validity, and construct validity with confirmatory factor analysis (CFA). Maximum likelihood parameter estimator with robust standard error estimation (MLR) was used for the CFA. Statistical analysis was performed using hierarchical (blockwise model) multiple regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Confirmatory factor analysis of the original factor structure showed that the structural model was a poor fit. After omitting two items with low standardized coefficients, the final, 14 item model’s fit was found to be good, with all coefficients being sufficiently high (ß\u0026gt;0.53) and the fit indices indicated also adequate fit (χ\u003csup\u003e2\u003c/sup\u003e=222.38, df=72, χ\u003csup\u003e2\u003c/sup\u003e/df=3.09 CFI=.94, IFI=.94, TLI=.92, RMSEA=.09 CI of RMSEA=.07-.10, SRMR=.05). The hierarchical multiple regression analysis revealed that intrinsic motivation, meaningful work and positive meaning positively predicted professional fulfillment with 59% of the total explained variance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: SPFI-H scales have adequate psychometric characteristics and offer a novel avenue for assessing professional fulfillment. This may extend our current knowledge on physicians’ well-being and inspire future investigations beyond focusing solely on burnout.\u003c/p\u003e","manuscriptTitle":"What makes physicians thrive? Assessing the mental health of Hungarian physicians with the Stanford Professional Fulfillment Index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 08:03:49","doi":"10.21203/rs.3.rs-6275614/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0260adb1-3afe-4b8d-885c-5a30a7d0805a","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T14:41:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 08:03:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6275614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6275614","identity":"rs-6275614","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}