Development of a premenstrual syndrome scale for working women and its validation against work productivity

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Methods In October 2021, we recruited 3,239 working women with menstruation via an internet research company and asked 47 questions about PMS-related symptoms. Results Of the participants, 331 women had experienced PMS (10%), and 393 women had taken sick leave because of PMS-associated symptoms (12%). Explanatory factor analyses with maximum likelihood and Promax rotation identified four domains with 27 items, including "Autonomic dysfunction symptoms'' (Cronbach’s α = 0.93), "Psychiatric symptoms" (Cronbach’s α = 0.94), "Lack of work efficiency" (Cronbach’s α = 0.93), and “Abdominal symptoms” (Cronbach’s α = 0.95). Using a split-half sample for the confirmatory factor analysis, moderately fit model indices for the four-factor solution were confirmed. We also confirmed the developed scale’s criterion validity using existing PMS screening criteria and its concurrent validity through high correlation coefficients with Copenhagen Burnout Inventory scores. The receiver operating characteristic curve yielded a good predictive ability for work absenteeism, including a sensitivity of 78%, a specificity of 57%, and an area under the curve of 0.735. Conclusion A highly reliable and valid new scale for PMS was developed with efficacy for screening for work absenteeism. Absenteeism Burnout Female workers Premenstrual Syndrome Scale development Figures Figure 1 Introduction According to a Japanese government survey or relevant source, more than 50% of female workers report that their work performance has been negatively affected by menstrual symptoms [ 1 , 2 ]. Another study [ 3 ] on 19,254 women aged 15–49 years showed that annual economic burden associated with severity of menstrual symptoms extrapolated to the Japanese female population was estimated to 8.6 billion United States Dollars. Among menstrual symptoms, premenstrual syndrome (PMS) is one of the most frequently reported in young working women and can induce depressive symptoms [ 4 – 6 ], especially in its most severe form, known as premenstrual dysphoric disorder (PMDD) [ 7 ], which is prevalent in 3–8% [ 8 ]. PMS/PMDD has been shown to decrease the quality of daily life [ 9 , 10 ] and can cause absenteeism, a form of greater work productivity loss [ 11 , 12 ]. PMDD is now defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 TR and is considered a mental illness [ 13 ]. Not only does PMS/PMDD cause women to miss work when symptoms appear, it can also lead to prolonged sick leaves. Furthermore, such symptoms can be exacerbated by different sources of workplace stress [ 2 , 14 ], including difficult human relationships, and can often cause invisible barriers to work productivity and, thus, mistreatment by male workers. If there is a judgement standard to assess what problems women face in the workplace due to menstrual symptoms, it would be possible to predict in advance the associated loss of work productivity and lead to strategic measures such as encouraging women to take menstrual leave [ 15 , 16 ]. To date, there are several assessment tools for menstrual distress symptoms developed. Among these, the Menstrual Distress Questionnaire [ 17 ] was one of the first diagnostic tools of premenstrual symptoms followed by the Premenstrual Tension Syndrome (PMTS) [ 18 ], and the Premenstrual Assessment Form (PAF) [ 19 ]. These predated the inclusion of Late Luteal Phase Dysphoric Disorder in the DSM and had limited psychometric properties [ 17 – 19 ]. Recently developed Premenstrual Symptoms Screening Tool (PSST) [ 13 ] aims to screen PMS/PMDD in accordance with DSM, which is biased toward psychometric characteristics. The Daily Record of Severity of Problems (DRSP) become the gold standard for diagnosis of PMDD, and Japanese version of DRSP have become available since 2021 [ 20 ]. Thus, the majority of previous screening tests for PMS has been biased toward psychological symptoms and less toward physical symptoms, and work productivity. Thus, the purpose of this study was (1) to develop a new screening tool for PMS that can be widely used in workplace, with questions related to various health aspects of female workers, including their physical and psychological symptoms, work productivity; and (2) to investigate the validity of the developed scale with Copenhagen Burnout Score Inventory, PSST, and absenteeism. Materials and methods Participants The survey was conducted by an internet research agency, GMO Research, Inc., between September 27 and 30, 2021. GMO first recruited 23,363 working women aged 18–41 years, because PMS is most prevalent in women in their late 20s to early 40s [ 6 ]. Of these, 3,880 agreed to participate in the study. Participants submitted an informed consent form via the company’s website ( https://gmo-research.jp ). The inclusion criteria were as follows: 1) salaried, working women, 2) menstruating women, and 3) Japanese-speaking population so that they were able to understand the meaning of each question. The exclusion criteria were as follows: 1) unemployed (n = 0), 2) postmenopausal status (n = 0), or 3) menstruation has temporarily stopped due to pregnancy, postpartum, medication, etc (n = 375). After the application of these eligibility criteria, 3,239 women were considered eligible for the analysis. The data set has no missing values. We asked about previous history of mental illness, including depression, and underlying gynecological illnesses, including endometriosis, myoma, and ovarian tumors. This study was approved by the Ethics Committee of the Akita University (No. 2712, approval date, July 7th, 2021), and performed in accordance with the Declaration of Helsinki. One of our members is involved in the management of a pharmaceutical company; however, we are not funded by that company, and none of our members have any conflicts of interest to disclose. Original items for scale development With reference to previous assessment scales for the diagnosis and screening of PMS, original items were developed by our research team, which included gynecologists, body-somatic medicine specialists, preconception research specialists, public health practitioners, data scientists, and executive representatives of a pharmaceutical company. In order to develop a comprehensive scale for the health status of female workers, original items were developed with reference to the previous literature, including the PSST [ 13 ]; Menstrual Distress Questionnaire (MDQ) Japanese Version [ 21 ]; Harvard Apple Women’s Health Study, which identified 15 menstrual period-related symptoms [ 22 ]; the Work Productivity and Activity Impairment Questionnaire: General Health V2.0 [ 23 ]; and Work-related Physical Activity Questionnaire (WPAQ) [ 24 ], which is a rating scale for physical activity and sedentary activity time by intensity at work. We then selected one representative item from similar items of no motivation (i.e., no motivation at work, house chores, and social interaction), from human relationship items (i.e., irritate, trouble, bumped, and conflict relationship), and from inability house chore (i.e., not capable of cleaning a room, washing, and cooking). We also discarded meaningless items (i.e., uber delivery, easily finger cut, absence from work) because these items are not symptoms and had multiple meanings. Subsequently, a final version of 47 questions to identify PMS-related symptoms of women workers was established (see the appendix). For each question, we asked respondents to indicate the severity of a symptom or event that began 1–2 weeks before the onset of menstruation and disappeared within 2 or 3 days after the onset of menstruation using a 5-point Likert scale, with 0 = no symptoms, 1 = slight symptoms, 2 = moderate symptoms, 3 = severe symptoms, and 4 = very severe symptoms. Among the above-mentioned evaluation methods, the PSST [ 13 ] is one of the most frequently used tools to identify PMS. This scale consists of 17 questions, of which 11 are related to psychological symptoms, 5 are related to impaired social functioning, and 1 is related to various physical symptoms, including breast tenderness, headache, joint or muscle pain, bloating, and weight gain [ 13 ]. In addition, the PSST [ 13 ] assesses whether any of the listed symptoms have interfered with the performance of daily activities. A combined scoring system that accounts for symptom severity and interference discriminates PMS from PMDD. Variables collected for validation Data were collected using self-administered questionnaires. In addition to the aforementioned 57 items related to PMS symptoms, the following items were also collected: age; BMI (kg/m 2 ); marital status; the presence of a child; family income; educational attainment; employment status; occupation; industry of employment; company size; working hours (i.e., average daily and weekly total working hours, night shift); regularity, amount, and length of menstruation; Copenhagen Burnout Inventory; and absenteeism from work productivity, including the experience of ever absenteeism and the longest sick leave period due to PMS. The International Standard Classification of Occupations was used for defining occupations and industries [ 25 ]. The Copenhagen Burnout Inventory measures burnout and is comprised of three domains (personal, work-related, and client-related). It has previously been shown that the higher the score on this inventory, the more likely a worker is to retire [ 26 ]. Statistical analysis For the scale development of PMS-related symptom scores, the participants were divided into two groups using the Bernoulli distribution. An exploratory factor analysis (EFA) was performed on one group and a Confirmatory factor analysis (CFA) on the other group [ 27 ]. CFA tests whether a relationship exists between an observed variable and its underlying latent variable. CFA validates the model structure of a developed scale by a priori determining the factor structure. We first performed the EFA and determined the number of factors based on a scree plot and the Kaiser criterion (eigenvalue > 1) with maximum likelihood estimation and Promax oblique rotation [ 27 ]. Items with factor loadings of < 0.5, items that were heavily loaded with two or more items, and items that were irrelevant to the domain classified or had duplicate meanings (e.g., no motivation instead of decreased interest in work, home, or social activities) were discarded. In determining the number of factors that should be retained, we used the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy over 0.5 and a significance level for the Bartlett’s test below 0.05 [ 27 ]. To determine the internal consistency of the items, we developed a final model and computed the item test, item-rest correlation, and Cronbach’s alpha and McDonald’s omega coefficients [ 28 ]. Once the final model was determined using EFA, we performed consecutive CFAs and computed the fit indices and factor loadings to confirm the best-fitting model with an root-mean-square error of approximation (RMSEA) of 0.9, Tucker–Lewis Index (TLI) of > 0.95, Standardized root mean squared (SRMS) (close to 0), and Coefficient of determination (CD) (close to 1) [ 27 ]. We repeatedly created a best-fit model until the lowest number of Akaike information criterion (AIC) statistics was reached. We used three types of validity if the newly developed scale is valid. Concurrent validity, which assesses how well a test correlates with a criterion measured at the same time, was tested by correlation with Copenhagen Burnout Inventory scores [ 26 ]. Criterion validity, which compares a new test with an existing test that is already considered valid or relevant to see if they produce similar results, tested using the chi-squared or Fisher’s exact tests between existing PMS and PMDD screening tools (i.e., PSST revised for adolescents questionnaire) and the binary scores of each factor and total scores divided by the median. Predictive validity, which measures how well a test predicts outcomes in the future (i.e., whether the developed scale could predict absenteeism) [ 29 ], we applied a logistic regression analysis and computed odds ratios with 95% confidence intervals. Multiple logistic regression was conducted with adjustments for previously burnout related covariates among Japanese working women [ 30 ] because burnout was considered as a proxy of absenteeism that eventually results in quitting a job: age, educational attainment, marital status, working status, and Copenhagen Burnout Inventory binary variables divided by the median. Finally, we drew receiver operating characteristic curves for absenteeism and estimated the area under the curve (AUC)s and cutoff points, with the optimal sensitivity and specificity based on the Youden Index. All analyses were performed using SAS (version 9.4, SAS Institute Inc., Cary, NC, USA) and Stata version 17 (Stata Corp., College Station, TX, USA). The significance level was two-sided and less than 0.05. Results The characteristics of the 3,239 participants (Tables 1 and 2 ) Table 1 The characteristics of 3239 participants N or Mean % or SD Age, mean ± SD 32.6 5.3 Body Mass Index, mean ± SD (missing n = 12) 20.5 3.2 Marital status Single 2251 69.5 Married 988 30.5 Children (+) 668 20.6 (-) 2571 79.4 Annual household income > 8 million JPY 682 21.1 6–8 million JPY 563 17.4 4–6 million JPY 771 23.8 2–4 million JPY 980 30.3 < 2 million JPY 243 7.5 Education attainment High/junior/elementary school 631 19.5 2 year-college 772 23.8 University/Graduated 1836 56.7 Occupation Clerical workers 1591 49.1 Service workers 580 17.9 Professional and engineering workers 499 15.4 Sales workers 172 5.3 Manufacturing process workers 125 3.9 Administrative and managerial workers 52 1.6 Others 220 6.8 Industry Medical, Health Care and Welfare 594 18.3 Manufacturing 463 14.3 Compound Services 350 10.8 Wholesale and Retail trade 312 9.6 Finance and Insurance 223 6.9 Construction 170 5.3 Education,Learning Support 153 4.7 Others 974 30.1 Gynecological underlying illness Myoma Uteri 157 4.85 Endometriosis 96 2.96 Ovarian cyst 84 2.59 Others 56 1.73 Psychological underlying illness Depression 9 0.28 Others 129 3.98 Table 2 The work characteristics of 3239 participants N or median % or IQR Working status Full-time worker 2494 77.0 Part-time worker 548 16.9 Self-employed 197 6.1 Workplace size 1000 workers 789 24.4 Labor characteristics Daily average hours of working, median (IQR) 8 (7–8) Weekly average hours of working, median (IQR) 40 (30–42) Daily average hours of standing, median (IQR) 2 (1–5) Numbers of carrying heavy object, N (%) 980 30.3 Numbers of a night shift in previous month, N (%) 340 10.5 Copenhagen Burnout Inventory, median (IQR) Personal Burnout 25/100 (8.3–50) Work related Burnout 39.3/100 (25.0-53.6) Client related Burnout 37.5/100 (20.8–54.2) Work productivity Ever been absent due to premenstrual symptoms Yes 393 12.1 No 2750 84.9 Do not remember 96 3.0 How long total absence at maximum (n = 393) months Reported n = 106 2 (1–5) weeks Reported n = 26 1 (1–2) days Reported n = 216 1 (1–1) hours Reported n = 45 3 (1–4) Our participants are female workers aged 18–41 years (excluding pregnant and lactating women) in Japan. The average age was 32.6 years old. In terms of occupational type, the majority of the women were clerical workers (49%), followed by service workers (18%) and professional/technical workers (15%). In terms of industry, 18% of the participants were in the medical and welfare sectors, 14% were in the manufacturing sector, 11% were in the service sector, and 10% were in the wholesale and retail sectors. In terms of working status, 77% were full-time workers, with a median of 8 daily working hours. In terms of work productivity, 393 people, or 12%, had been absent due to premenstrual symptoms, with 106 people (27%) being absent for at least 1 year, 26 people (7%) being absent for 1 week, and 216 people (55%) being absent for 1 day. Scale development: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) Table 3 presents the results of the EFA and CFA [ 27 ]. The initial solution based on a scree plot with eigenvalues of 1.0 or greater suggested four factors. Using a split-half sample, we performed EFA, excluded items that were either cross-loaded or did not load enough to exceed 0.5, and repeated this process until we reached the lowest AIC in the CFA. This process eventually resulted in four domains and 27 items. We retained four factors based on the KMO measure (0.977) and Bartlett’s test below 0.05. The final model was investigated with the requirement of a well-fitted model (RMSEA = 0.077, CFI = 0.928, a TLI = 0.921, SRMS = 0.043, CD = 1.000) [ 27 ]. The domains included "Autonomic dysfunction symptoms'' (13 items, Cronbach’s α = 0.960, Macdonald’s omega = 0.961), "Psychiatric symptoms" (6 items, Cronbach’s α = 0.933, Macdonald’s omega = 0.934), "Lack of work efficiency" (5 items, Cronbach’s α = 0.947, Macdonald’s omega = 0.948), and “Abdominal symptoms” (3 items, Cronbach’s α = 0.821, Macdonald’s omega = 0.827) [ 28 ]. The "Autonomic dysfunction symptoms" had the highest factor loading, ranging between 0.528 and 0.892, followed by the "Psychiatric symptoms," ranging between 0.590 and 0.884, and the "Lack of work efficiency," ranging between 0.541 and 0.796, while the "Abdominal symptoms" had the smallest factor loadings, ranging between 0.604 and 0.703. The Cronbach’s α values for each factor exceeded 0.8, McDonald's omega exceeded 0.7, and the item correlations between each factor were reasonably high, suggesting good reliability and a high internal consistency for each factor. Table 3 The results of Exploratory and Confirmatory Factor Analysis Exploratory Factor Analysis (n = 1597; Maximum likelihood, Promax rotation) Factor1 Factor2 Factor3 Factor4 Factor 1 "Autonomic dysfunction symptoms", α = 0.960, omega = 0.961 Difficulty in breathing 0.722 0.167 0.033 -0.053 Dizziness 0.536 0.156 -0.005 0.238 Stumbling or falling over 0.800 0.027 0.088 -0.034 Nausea or vomiting 0.687 -0.032 0.030 0.203 Hot flushes on face or upper body 0.591 0.098 0.043 0.149 Chest pain or squeezing in the chest 0.759 0.015 -0.059 0.128 Tinnitus 0.857 0.003 -0.023 -0.033 Heart beating or pounding 0.681 0.194 0.058 -0.019 Numbness in the limbs 0.892 -0.040 0.010 -0.047 Chilly lower back or limbs 0.528 0.068 0.078 0.165 Blurred vision or difficulty in seeing 0.799 0.054 0.048 -0.078 Pain in finger or knee joint 0.731 -0.062 0.113 0.082 Muscle pain (clumps in the legs) 0.783 -0.088 0.128 0.035 Factor 2 "Psychiatric symptoms", α = 0.933, omega = 0.934 Mood Depression 0.056 0.884 -0.053 0.028 Anxiety 0.216 0.820 -0.007 -0.136 Feeling of extreme psychological instability 0.056 0.880 -0.018 -0.017 Irritability -0.075 0.719 -0.006 0.158 Lack of motivation to work -0.075 0.622 0.223 0.097 Lack of concentration -0.062 0.590 0.290 0.122 Factor 3 "Lack of work efficiency", α = 0.947, omega = 0.948 Expending more time on routine work 0.290 0.035 0.669 -0.002 Feeling of lower accomplishment at work 0.127 0.081 0.796 -0.007 Increased careless mistakes at work 0.161 0.129 0.709 -0.029 Expending more time in finishing usual household chores 0.293 0.075 0.541 0.033 Reduced working and learning ability 0.053 0.249 0.561 0.122 Factor 4 "Abdominal symptoms", α = 0.821, omega = 0. 827 Diarrhea or constipation 0.101 0.077 0.024 0.604 Abdominal distention 0.076 0.110 0.044 0.703 Abdominal pain, cramps 0.089 0.070 -0.021 0.673 Factor correlation (n = 1597) Factor1 1.000 Factor2 0.553 1.000 Factor3 0.675 0.643 1.000 Factor4 0.576 0.585 0.594 1.000 Confirmatory Factor Analysis Result (n = 1642) Root mean squared error of approximation 0.077 (0.074–0.079) Akaike's information criterion 79438 Comparative fit index 0.928 Tucker-Lewis index 0.921 Standardized root mean squared 0.043 Coefficient of determination 1.000 Validation of the developed scale: concurrent, criterion, and predictive validity Table 4 shows the Spearman’s correlation coefficients between the developed scale and the Copenhagen Burnout Inventory scores [ 26 ]. We confirmed a high correlation with the three subscales of the Copenhagen Burnout Inventory (all P s < 0.0001), suggesting a high concurrent validity. Table 5 presents the criterion validity between the developed scale and PMS and PMDD based on the Premenstrual Symptoms Screening Tool (PSST) [ 13 ]. Due to the small values for PMS and PMDD, we applied the chi-squared or Fisher’s exact tests and found that women who were in the upper half for each factor and the total score, were more likely to have PMS or PMDD (Table 5 , all P s < 0.0001). This indicates that each factor in the scale developed had a high predictability of PMS or PMDD. Table 4 Concurrent validity with Spearman correlation coefficient between the newly developed scale and the Copenhagen burnout score # of item median (IQR) Copenhagen Burnout Inventory Personal Burnout Work related Burnout Client related Burnout Factor 1 "Autonomic dysfunction symptoms" 13 15 (13–21) 0.515* 0.435* 0.409* Factor 2 "Psychiatric symptoms" 6 11 (8–16) 0.626* 0.564* 0.522* Factor 3 " Lack of work efficiency " 5 6 (5–10) 0.545* 0.499* 0.472* Factor 4 " Abdominal symptoms " 3 5 (3–7) 0.480* 0.404* 0.414* Total score 27 39 (31–53) 0.628* 0.548* 0.517* * p < 0.0001 Table 5 Criterion validity between the developed scale and PMS and PMDD based on the Premenstrual Symptom Screening Tool PMS (n = 331, 10%) PMDD (n = 99, 3%) (+) (-) P* (+) (-) P N (%) N (%) N (%) N (%) Factor 1 "Autonomic dysfunction symptoms" < .0001 < .0001 Upper half (n = 1692) 326 98.5 1366 47 99 100 1593 50.7 Lower half (n = 1547) 5 1.5 1542 53 0 0 1547 49.3 Factor 2 "Psychiatric symptoms" < .0001 < .0001 Upper half (n = 1787) 330 99.7 1457 50.1 99 100 1688 53.8 Lower half (n = 1452) 1 0.3 1451 49.9 0 0 1452 46.2 Factor 3 " Lack of work efficiency " < .0001 < .0001 Upper half (n = 1699) 329 99.4 1370 47.1 98 99.0 1601 51.0 Lower half (n = 1540) 2 0.6 1538 52.9 1 1.0 1539 49.0 Factor 4 " Abdominal symptoms" < .0001 < .0001 Upper half (n = 1994) 320 96.7 1674 57.6 95 96.0 1899 60.5 Lower half (n = 1245) 11 3.3 1234 42.4 4 4.0 1241 39.5 Total score < .0001 < .0001 Upper half (n = 1627) 330 99.7 1297 44.6 99 100 1528 48.7 Lower half (n = 1612) 1 0.3 1611 55.4 0 0 1612 51.3 *Based on Chi-square test or Fisher's exact test Table 6 shows the validation of the developed scale for work absenteeism by logistic regression models. Univariate associations using chi-squared testing or logistic regression models demonstrated that women who scored in the upper half for each factor and the total score were more likely to have ever had absenteeism from work. After adjusting for age, educational attainment, marital status, working status, and Copenhagen Burnout Inventory scores, women with who scored in the upper half for “Autonomic dysfunction symptoms” were 2.13-2.59-fold more likely, those with “Psychiatric symptoms” were 1.81-2.15-fold more likely, those with "Lack of work efficiency" were 1.96-2.36-fold more likely, and those with " Abdominal symptoms " were 2.05-2.56-fold more likely to have ever had absenteeism. Although OR of each factor in PBO, WBO, and CBO falls in each 95% CI, indicating no statistical difference, the OR of each factor is statistically associated with work absenteeism. Thus, the developed scale appeared to yield high predictive validity [ 29 ]. Table 6 The results of the logistic regression model validation of the absenteeism scale Work absenteeism experience due to PMS related symptoms Logistic regression models for Work absenteeism Yes (n = 393) No/Do not (n = 2846) P* Crude odds ratio (95% CI) Copenhagen burnout inventory adjust odds ratio (95% CI)* N (%) N (%) PBO model WBO model CBO model Factor1 "Autonomic dysfunction symptoms " < .0001 Upper half (n = 1692) 299 76.1 1393 49.0 3.32 (2.60–4.23) 2.44 (1.88–3.17) 2.59 (2.01–3.34) 2.13 (1.67–2.73) Lower half (n = 1547) 94 23.9 1453 51.1 1 1 1 1 Factor 2 "Psychiatric symptoms" < .0001 Upper half (n = 1787) 324 82.4 1463 51.4 4.44 (3.39–5.82) 2.09 (1.60–2.73) 2.15 (1.65–2.79) 1.81 (1.41–2.33) Lower half (n = 1452) 69 17.6 1383 48.6 1 1 1 1 Factor 3 "Lack of work efficiency" < .0001 Upper half (n = 1699) 309 78.6 1390 48.8 3.85 (2.99–4.96) 2.31 (1.78-3.00) 2.36 (1.82–3.06) 1.96 (1.52–2.51) Lower half (n = 1540) 84 21.4 1456 51.2 1 - - - Factor 4 " Abdominal symptoms " < .0001 Upper half (n = 1994) 337 85.8 1657 58.2 4.32 (3.22–5.79) 2.47 (1.91–3.18) 2.56 (1.99–3.29) 2.05 (1.61–2.62) Lower half (n = 1245) 56 14.3 1189 41.8 1 1 1 1 Total score < .0001 Upper half (n = 1627) 319 81.2 1308 46.0 5.07 (3.90–6.60) 1.84 (1.40–2.42) 1.99 (1.52–2.59) 1.68 (1.30–2.17) Lower half (n = 1612) 74 18.8 1538 54.0 1 1 1 1 *Based on chi-square test. **Adjusted for age, educational attainment, marital status, working status, and Copenhagen burnout binary variable divided by median. Predictive performance of the developed scale Based on the largest Youden index [ 31 ] to establish an optimal-thresholds, we estimated the total score of the disability index to be 40 points, with a sensitivity of 79%, specificity of 57%, positive predictive value of 20%, negative predictive value of 95%, positive likelihood ratio of 1.83, negative likelihood ratio of 0.36, and AUC of 0.735 (95% CI: 0.710–0.760; Fig. 1 ), which were deemed acceptable after referencing the Hosmer and Lemeshow test [ 32 ]. Discussion Short summary In this study, we examined 3,239 female workers aged 18–41 years (excluding pregnant and lactating women) in Japan as PMS is most prevalent in women in their late 20s to early 40s [ 6 , 33 ]. Of the initial 47 items we considered, the EFA identified four domains with 27 items, which was then determined to be a moderately fitted model using the CFA. The four domains included "Autonomic dysfunction symptoms'' (13 items), "Psychiatric symptoms" (6 items), "Lack of work efficiency" (5 items), and “Abdominal symptoms” (3 items), with high Cronbach’s alphas and item correlations. In addition, the developed scale had a high concurrent validity, which refers to a high correlation with the three subscales of the Copenhagen Burnout Inventory [ 26 ], and a high criterion validity due to significant associations with PMS or PMDD [ 13 ]. Finally, we confirmed that the developed scale is capable of predicting work absenteeism caused by PMS-related symptoms, with an acceptable AUC. These results suggest that the scale developed in this study may be useful for helping working women to stay healthy and maximize their work productivity. Interpretation of the findings The strength of this study is that we included a variety of symptoms both directly and indirectly related to PMS according to previous literature. Unfortunately, there have been no reference guidelines previously published for PMS. Also, PMDD has only recently been included in DSM-5 as a disease entity, with a heavy weighting on psychological symptoms [ 34 , 35 ]. Similarly, the PSST was previously developed by Steiner [ 13 ]; however, physical symptoms were treated as only one of a total of 14 symptoms, of which 10 symptoms were psychological. In contrast, our scale is balanced and evenly divided into both physical (i.e., Autonomic dysfunction) and psychological factors, as well as the two other domains of a lack of work efficiency and abdominal symptoms. Our scale can be widely applied in the workplace to screen for potential absenteeism, which is very useful in terms of work productivity loss. Implications Female workers, corporations, and society need a comprehensive scale to evaluate PMS in the workplace. Therefore, our newly developed tool might be utilized as a scale for corporations to screen for female workers who struggle with PMS-related issues. This tool not only helps corporations identify and support female workers with PMS but also contributes to a more inclusive and supportive work environment. Existing scales like the DSM-5 [ 36 ] and PSST [ 13 ] are clinical questionnaires with a heavy weighting on psychological items; however, our developed scale is more balanced and practical for the workplace because it contains more comprehensive categories, including physical (i.e., Autonomic dysfunction) and psychological factors, a lack of work efficiency and abdominal symptoms. In addition, while the other clinical scales overlook work-related aspects for high-risk individuals, the new tool may detect female employees who are highly likely to be absent from work due to these symptoms. However, again, this scale is not meant to discriminate against women who suffer from menstrual symptoms but to screen women who might need further medical treatment. The scale measures the health status of women workers at the time of investigation but can be repeatedly used whenever a woman worker manifests menstrual symptoms. As this is a screening test, it is recommended that a positive result should be followed by a visit to a gynecologist or family doctor for a detailed diagnosis of PMS/PMDD and, if necessary, medication treatment. To summarize, the use of the developed scale promotes understanding of PMS within a company and might even lead to timely diagnosis of untreated underlying mental or gynecological illness (endometriosis, fibroids, etc.) that contributes to the possibility of increased work productivity. This scale, which was validated against existing PMS and PMDD scales, can help measure individual burdens in female workers suffering from PMS and can also be used by employers to assess women’s health status. Study limitations There are several study limitations that should be examined and discussed. First, in this study, only 10% of women had PMS and 3% had PMDD, which was slightly less than the 20–30% observed in community samples [ 35 ] and the 5.9% of 303 healthy women aged 20–45 years [ 7 ] assessed using the DSM-5 [ 36 ], which suggests that our study participants might have been healthier than these other populations. Second, according to a labor survey conducted by the Ministry of Internal Affairs and Communications in 2021, the most significant proportion of women (22%) are employed in the health and welfare industry, with the second most common industry being wholesale and retail trade at 17% [ 37 ]. In this study, the proportion of individuals employed in the health and welfare industries was slightly smaller (18.5%), followed by manufacturing at 14.3%. The difference between our sample and whole women workers in Japan may not physically burden our participants since our study participants reported an average of only 8 hours per day and a median of only 2 hours of standing per day. This working condition might have influenced (reduced) the severity of PMS-associated symptoms [ 38 ]. Third, we only investigated the predictability of absenteeism because the definition clearly depends on the numbers of periods (i.e., days, months) due to menstrual symptoms compared to presenteeism which relies on self-reporting though it has been previously suggested that menstrual symptoms may affect presenteeism rather than absenteeism [ 12 , 39 , 40 ]. In this regard, we believe that this scale should not be used to discriminate in hiring processes. It would be important from an ethical perspective to address this concern. We only focused on absenteeism because of the financial losses incurred by individual companies, which can be justified in terms of corporate financial management. Fourth, in the previously developed PMS scale, PSST [ 13 ], physical symptoms were treated as only one of a total of 14 symptoms, of which 10 symptoms were psychological and the other three symptoms were related to a lack of motivation for work, household chores, or social interactions. In our study, as these three items are very similar, we replaced them with a single item devoted to motivation. Similarly, we employed one item instead of multiple similar items, such as four human relationships (irritated, troubled, disputed, and conflicted relationships) and three functional items (being unable to clean a room, washing, and cooking). Eventually, these items did not load further and were discarded. Fifth, a previous report [ 41 ] discussed how early-life stress influences the development of PMDD through hormonal and stress-response pathways. In our study, we asked for psychiatric history/diagnosis, including depression, but never asked for a history of childhood trauma, particularly emotional abuse and neglect, that might increase susceptibility for PMS/PMDD. Sixth, although our participants could potentially be of any nationality, the responses required at least a minimum level of Japanese language comprehension; therefore, we can assume that the majority of respondents were Japanese. Seventh, the scale developed in this study is not a diagnostic tool and, thus, would require medical evaluation to confirm or determine other causes of self-reported symptoms (i.e., PMDD or other psychiatric or gynecological illness). Finally, although our developed disability index is highly reliable and valid, the AUC was 0.7 [ 32 ], which suggests a moderate acceptability. The moderate level of AUC is explained by absenteeism, a form of work productivity to predict, which is usually a clinical outcome. Because the scale developed in this study is meant to identify PMS but not absenteeism, obtaining a high level of AUC may be difficult. Considering these limitations, our results should be interpreted with caution. Conclusion We developed a reliable and valid PMS screening tool for women workers. This scale would allow labor managers, business owners, and working women to detect PMS earlier. An appropriate intervention would resolve the health aspects of female workers, including their physical and psychological symptoms, work efficiency, and productivity. Abbreviations PMS premenstrual syndrome EFA exploratory factor analyses CFA confirmatory factor analyses CBI Copenhagen Burnout Inventory PMDD premenstrual dysphoric disorder DSM Diagnostic and Statistical Manual of Mental Disorders PMTS Premenstrual Tension Syndrome PAF Premenstrual Assessment Form PSST Premenstrual Symptoms Screening Tool DRSP Daily Record of Severity of Problems MDQ Menstrual Distress Questionnaire WPAQ Work-related Physical Activity Questionnaire KMO Kaiser–Meyer–Olkin RMSEA root-mean-square error of approximation CFI Comparative fit index TLI Tucker–Lewis Index CD Coefficient of determination AIC Akaike information criterion AUC area under the curve Declarations Acknowledgement We are very grateful to all the participants in this research project. Author Contributions Concept and design: KN, CT; Collection of data: KN, EM, CT; Analysis and interpretation of data: CT, KS, MI, FT, EM, SJ, and KN; Revision of the paper: CT, KS, MI, FT, EM, SJ, and KN. All authors read and approved the final manuscript. Funding This study was funded by the Ministry of Education, Culture, Sports, Science and Technology, Japan (Grants for Scientific Research [B], Number 21H03192), and Akita University Graduate School of Medicine. Data Availability Statement The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request. Ethics statement This study was approved by the Ethics Committee of Akita University (No. 2712, approval date, July 7th, 2021). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Consent for publication Not applicable. Conflict of Interest The authors declare that the research was conducted without any commercial or financial relationships that could potentially create a conflict of interest. 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General Health V2.0 (WPAI:GH) 2018. https://eprovide.mapi-trust.org/instruments/work-productivity-and-activity-impairment-questionnaire-general-health-v2.0 . Accessed 24 December 2024. Fukushima N, Amagasa S, Kikuchi H, Takamiya T, Odagiri Y, Hayashi T et al. [Validity and reliability of the Work-related Physical Activity Questionnaire for assessing intensity-specific physical activity and sedentary behavior in the workplace]. Sangyo Eiseigaku Zasshi. 2020;62(2):61-71.10.1539/sangyoeisei.2019-013-B. The History of the Japan Standard Occupational Classification. 2010. https://www.soumu.go.jp/main_content/000327409.pdf . Accessed 3 November 2024. Kristensen TS, Borritz M, Villadsen E, Christensen KB. The Copenhagen Burnout Inventory: A new tool for the assessment of burnout. Work Stress. 2005;19(3):192–207. Orçan F. Exploratory and confirmatory factor analysis: which one to use first? J Meas Evaluation Educ Psychol. 2018;9(4):414–21. Viladrich C, Angulo-Brunet A, Doval E. A journey around alpha and omega to estimate internal consistency reliability. Anales de psicología. 2017;33(3):755–82. Ekblom Ö, Ekblom-Bak E, Bolam KA, Ekblom B, Schmidt C, Söderberg S et al. Concurrent and predictive validity of physical activity measurement items commonly used in clinical settings–data from SCAPIS pilot study. BMC Public Health. 2015;15:978.10.1186/s12889-015-2316-y. Perumalswami CR, Takenoshita S, Tanabe A, Kanda R, Hiraike H, Okinaga H et al. Workplace resources, mentorship, and burnout in early career physician-scientists: a cross sectional study in Japan. BMC Med Educ. 2020;20(1):178.10.1186/s12909-020-02072-x. Chen F, Xue Y, Tan MT, Chen P. Efficient statistical tests to compare Youden index: accounting for contingency correlation. Stat Med. 2015;34(9):1560 – 76.10.1002/sim.6432. Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression: Wiley; 2013. ACOG-Clinical. Management of Premenstrual Disorders: ACOG Clinical Practice Guideline No. 7. Obstet Gynecol. 2023;142(6):1516 – 33.10.1097/aog.0000000000005426. Hantsoo L, Epperson CN. Premenstrual Dysphoric Disorder: Epidemiology and Treatment. Curr Psychiatry Rep. 2015;17(11):87.10.1007/s11920-015-0628-3. Naik SS, Nidhi Y, Kumar K, Grover S. Diagnostic validity of premenstrual dysphoric disorder: revisited. Front Glob Womens Health. 2023;4:1181583.10.3389/fgwh.2023.1181583. American Psychiatric Association, Association D. AP. Diagnostic and statistical manual of mental disorders: DSM-5. American psychiatric association Washington, DC; 2013. Annual Report of the Labor Force Survey. 2021. https://www.stat.go.jp/data/roudou/report/2021/index.html . Accessed 3 November 2024. Mohebbi Dehnavi Z, Jafarnejad F, Sadeghi Goghary S. The effect of 8 weeks aerobic exercise on severity of physical symptoms of premenstrual syndrome: a clinical trial study. BMC Womens Health. 2018;18(1):80.10.1186/s12905-018-0565-5. Uchibori M, Eguchi A, Ghaznavi C, Tanoue Y, Ueta M, Sassa M et al. Understanding factors related to healthcare avoidance for menstrual disorders and menopausal symptoms: A cross-sectional study among women in Japan. Prev Med Rep. 2023;36:102467.10.1016/j.pmedr.2023.102467. Goetzel RZ, Long SR, Ozminkowski RJ, Hawkins K, Wang S, Lynch W. Health, absence, disability, and presenteeism cost estimates of certain physical and mental health conditions affecting U.S. employers. J Occup Environ Med. 2004;46(4):398-412.10.1097/01.jom.0000121151.40413.bd. Wei S, Tang YY, Wang F, Wang Y, Geng X, Editorial. Neural circuits and neuroendocrine mechanisms of depression and premenstrual dysphoric disorder: towards precise targets for translational medicine and drug development, volume II. Front Psychiatry. 2023;14:1216689.10.3389/fpsyt.2023.1216689. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6644419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466903629,"identity":"8dd42cbd-219f-4986-a17b-fd594d22cbec","order_by":0,"name":"Chihiro Takenoshita","email":"","orcid":"","institution":"Akita University","correspondingAuthor":false,"prefix":"","firstName":"Chihiro","middleName":"","lastName":"Takenoshita","suffix":""},{"id":466903630,"identity":"2cfc485b-8434-4b88-9f89-3a2c32266574","order_by":1,"name":"Kisho Shimizu","email":"","orcid":"","institution":"Akita University","correspondingAuthor":false,"prefix":"","firstName":"Kisho","middleName":"","lastName":"Shimizu","suffix":""},{"id":466903631,"identity":"9bfe4c03-ae78-4db2-bc58-dd852f56715f","order_by":2,"name":"Miho Iida","email":"","orcid":"","institution":"Keio University","correspondingAuthor":false,"prefix":"","firstName":"Miho","middleName":"","lastName":"Iida","suffix":""},{"id":466903632,"identity":"d45b97db-4bd2-4a60-abdd-743c0fd32a79","order_by":3,"name":"Fumiaki Taka","email":"","orcid":"","institution":"Toyo University","correspondingAuthor":false,"prefix":"","firstName":"Fumiaki","middleName":"","lastName":"Taka","suffix":""},{"id":466903633,"identity":"ac788e43-8986-4e31-8520-22c80f67fd17","order_by":4,"name":"Eri Maeda","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Eri","middleName":"","lastName":"Maeda","suffix":""},{"id":466903634,"identity":"e2c66070-f6fa-4673-b5b3-ab1abea9b0b1","order_by":5,"name":"Songee Jung","email":"","orcid":"","institution":"Akita University","correspondingAuthor":false,"prefix":"","firstName":"Songee","middleName":"","lastName":"Jung","suffix":""},{"id":466903635,"identity":"a9bfc1a2-0b67-4936-98b6-ca312c788f33","order_by":6,"name":"Kyoko Nomura","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACCSgtB8QGDAwHJOAyzIS0GMO0wPUQ1JLYANHCIIFLJRxIzm4+upl3j1362vbmDQw/zljU8TfwGDD8qGFgN8ehRVrmWNptnmfJudvOHCtg7LkhISFxgMeAsecYA7NlA3YtchI5Zrd5DjDnbruRY8DA8wHol/tvDBh4GxiYDQ7g1VKfbgZUyfgHqEUeZMtfPFqkIVoOJ5jd4DFg5gE6zACohRmfLZIz0tJuzjlw3HDbmbSCwzJnJCQ3HmADMo5J4PSLxI3kYzfeHKiWNzt+eOPDN8fq+OUOMAMZNTbJuEIMBRxAYkgkGxCjBQXYka5lFIyCUTAKhikAAJ4HWJpa+tu8AAAAAElFTkSuQmCC","orcid":"","institution":"Akita University","correspondingAuthor":true,"prefix":"","firstName":"Kyoko","middleName":"","lastName":"Nomura","suffix":""}],"badges":[],"createdAt":"2025-05-12 08:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6644419/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6644419/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-025-04092-5","type":"published","date":"2025-11-07T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84329098,"identity":"c6b8ab9b-51fb-4d15-bbf8-c5233f851b3c","added_by":"auto","created_at":"2025-06-10 15:39:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1774215,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve of the scale for absenteeism\u003c/p\u003e\n\u003cp\u003eUnder the Optimal Youden best cut-off point 40 of total score, sensitivity yields 79%, specificity yields 57%, and area under the curve yields 0.735.\u003c/p\u003e","description":"","filename":"2Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6644419/v1/32ead6b773a1ed48b69fd733.png"},{"id":95564055,"identity":"98fc6a18-8c20-4398-94fc-f2f0b64b5817","added_by":"auto","created_at":"2025-11-10 16:07:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3475971,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6644419/v1/12cf718c-db95-4791-99d0-094a857656b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a premenstrual syndrome scale for working women and its validation against work productivity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to a Japanese government survey or relevant source, more than 50% of female workers report that their work performance has been negatively affected by menstrual symptoms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Another study [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] on 19,254 women aged 15\u0026ndash;49 years showed that annual economic burden associated with severity of menstrual symptoms extrapolated to the Japanese female population was estimated to 8.6\u0026nbsp;billion United States Dollars. Among menstrual symptoms, premenstrual syndrome (PMS) is one of the most frequently reported in young working women and can induce depressive symptoms [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], especially in its most severe form, known as premenstrual dysphoric disorder (PMDD) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which is prevalent in 3\u0026ndash;8% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. PMS/PMDD has been shown to decrease the quality of daily life [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and can cause absenteeism, a form of greater work productivity loss [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. PMDD is now defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 TR and is considered a mental illness [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Not only does PMS/PMDD cause women to miss work when symptoms appear, it can also lead to prolonged sick leaves. Furthermore, such symptoms can be exacerbated by different sources of workplace stress [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], including difficult human relationships, and can often cause invisible barriers to work productivity and, thus, mistreatment by male workers. If there is a judgement standard to assess what problems women face in the workplace due to menstrual symptoms, it would be possible to predict in advance the associated loss of work productivity and lead to strategic measures such as encouraging women to take menstrual leave [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, there are several assessment tools for menstrual distress symptoms developed. Among these, the Menstrual Distress Questionnaire [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was one of the first diagnostic tools of premenstrual symptoms followed by the Premenstrual Tension Syndrome (PMTS) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and the Premenstrual Assessment Form (PAF) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These predated the inclusion of Late Luteal Phase Dysphoric Disorder in the DSM and had limited psychometric properties [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recently developed Premenstrual Symptoms Screening Tool (PSST) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] aims to screen PMS/PMDD in accordance with DSM, which is biased toward psychometric characteristics. The Daily Record of Severity of Problems (DRSP) become the gold standard for diagnosis of PMDD, and Japanese version of DRSP have become available since 2021 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, the majority of previous screening tests for PMS has been biased toward psychological symptoms and less toward physical symptoms, and work productivity.\u003c/p\u003e \u003cp\u003eThus, the purpose of this study was (1) to develop a new screening tool for PMS that can be widely used in workplace, with questions related to various health aspects of female workers, including their physical and psychological symptoms, work productivity; and (2) to investigate the validity of the developed scale with Copenhagen Burnout Score Inventory, PSST, and absenteeism.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe survey was conducted by an internet research agency, GMO Research, Inc., between September 27 and 30, 2021. GMO first recruited 23,363 working women aged 18\u0026ndash;41 years, because PMS is most prevalent in women in their late 20s to early 40s [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Of these, 3,880 agreed to participate in the study. Participants submitted an informed consent form via the company\u0026rsquo;s website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gmo-research.jp\u003c/span\u003e\u003cspan address=\"https://gmo-research.jp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The inclusion criteria were as follows: 1) salaried, working women, 2) menstruating women, and 3) Japanese-speaking population so that they were able to understand the meaning of each question. The exclusion criteria were as follows: 1) unemployed (n\u0026thinsp;=\u0026thinsp;0), 2) postmenopausal status (n\u0026thinsp;=\u0026thinsp;0), or 3) menstruation has temporarily stopped due to pregnancy, postpartum, medication, etc (n\u0026thinsp;=\u0026thinsp;375). After the application of these eligibility criteria, 3,239 women were considered eligible for the analysis. The data set has no missing values. We asked about previous history of mental illness, including depression, and underlying gynecological illnesses, including endometriosis, myoma, and ovarian tumors.\u003c/p\u003e \u003cp\u003eThis study was approved by the Ethics Committee of the Akita University (No. 2712, approval date, July 7th, 2021), and performed in accordance with the Declaration of Helsinki. One of our members is involved in the management of a pharmaceutical company; however, we are not funded by that company, and none of our members have any conflicts of interest to disclose.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOriginal items for scale development\u003c/h3\u003e\n\u003cp\u003eWith reference to previous assessment scales for the diagnosis and screening of PMS, original items were developed by our research team, which included gynecologists, body-somatic medicine specialists, preconception research specialists, public health practitioners, data scientists, and executive representatives of a pharmaceutical company. In order to develop a comprehensive scale for the health status of female workers, original items were developed with reference to the previous literature, including the PSST [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; Menstrual Distress Questionnaire (MDQ) Japanese Version [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; Harvard Apple Women\u0026rsquo;s Health Study, which identified 15 menstrual period-related symptoms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; the Work Productivity and Activity Impairment Questionnaire: General Health V2.0 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; and Work-related Physical Activity Questionnaire (WPAQ) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which is a rating scale for physical activity and sedentary activity time by intensity at work. We then selected one representative item from similar items of no motivation (i.e., no motivation at work, house chores, and social interaction), from human relationship items (i.e., irritate, trouble, bumped, and conflict relationship), and from inability house chore (i.e., not capable of cleaning a room, washing, and cooking). We also discarded meaningless items (i.e., uber delivery, easily finger cut, absence from work) because these items are not symptoms and had multiple meanings.\u003c/p\u003e \u003cp\u003eSubsequently, a final version of 47 questions to identify PMS-related symptoms of women workers was established (see the appendix). For each question, we asked respondents to indicate the severity of a symptom or event that began 1\u0026ndash;2 weeks before the onset of menstruation and disappeared within 2 or 3 days after the onset of menstruation using a 5-point Likert scale, with 0\u0026thinsp;=\u0026thinsp;no symptoms, 1\u0026thinsp;=\u0026thinsp;slight symptoms, 2\u0026thinsp;=\u0026thinsp;moderate symptoms, 3\u0026thinsp;=\u0026thinsp;severe symptoms, and 4\u0026thinsp;=\u0026thinsp;very severe symptoms.\u003c/p\u003e \u003cp\u003eAmong the above-mentioned evaluation methods, the PSST [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] is one of the most frequently used tools to identify PMS. This scale consists of 17 questions, of which 11 are related to psychological symptoms, 5 are related to impaired social functioning, and 1 is related to various physical symptoms, including breast tenderness, headache, joint or muscle pain, bloating, and weight gain [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, the PSST [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] assesses whether any of the listed symptoms have interfered with the performance of daily activities. A combined scoring system that accounts for symptom severity and interference discriminates PMS from PMDD.\u003c/p\u003e\n\u003ch3\u003eVariables collected for validation\u003c/h3\u003e\n\u003cp\u003eData were collected using self-administered questionnaires. In addition to the aforementioned 57 items related to PMS symptoms, the following items were also collected: age; BMI (kg/m\u003csup\u003e2\u003c/sup\u003e); marital status; the presence of a child; family income; educational attainment; employment status; occupation; industry of employment; company size; working hours (i.e., average daily and weekly total working hours, night shift); regularity, amount, and length of menstruation; Copenhagen Burnout Inventory; and absenteeism from work productivity, including the experience of ever absenteeism and the longest sick leave period due to PMS. The International Standard Classification of Occupations was used for defining occupations and industries [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The Copenhagen Burnout Inventory measures burnout and is comprised of three domains (personal, work-related, and client-related). It has previously been shown that the higher the score on this inventory, the more likely a worker is to retire [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor the scale development of PMS-related symptom scores, the participants were divided into two groups using the Bernoulli distribution. An exploratory factor analysis (EFA) was performed on one group and a Confirmatory factor analysis (CFA) on the other group [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. CFA tests whether a relationship exists between an observed variable and its underlying latent variable. CFA validates the model structure of a developed scale by a priori determining the factor structure. We first performed the EFA and determined the number of factors based on a scree plot and the Kaiser criterion (eigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;1) with maximum likelihood estimation and Promax oblique rotation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Items with factor loadings of \u0026lt;\u0026thinsp;0.5, items that were heavily loaded with two or more items, and items that were irrelevant to the domain classified or had duplicate meanings (e.g., no motivation instead of decreased interest in work, home, or social activities) were discarded. In determining the number of factors that should be retained, we used the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) measure of sampling adequacy over 0.5 and a significance level for the Bartlett\u0026rsquo;s test below 0.05 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To determine the internal consistency of the items, we developed a final model and computed the item test, item-rest correlation, and Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega coefficients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Once the final model was determined using EFA, we performed consecutive CFAs and computed the fit indices and factor loadings to confirm the best-fitting model with an root-mean-square error of approximation (RMSEA) of \u0026lt;\u0026thinsp;0.08, Comparative fit index (CFI) of \u0026gt;\u0026thinsp;0.9, Tucker\u0026ndash;Lewis Index (TLI) of \u0026gt;\u0026thinsp;0.95, Standardized root mean squared (SRMS) (close to 0), and Coefficient of determination (CD) (close to 1) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We repeatedly created a best-fit model until the lowest number of Akaike information criterion (AIC) statistics was reached.\u003c/p\u003e \u003cp\u003eWe used three types of validity if the newly developed scale is valid. Concurrent validity, which assesses how well a test correlates with a criterion measured at the same time, was tested by correlation with Copenhagen Burnout Inventory scores [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Criterion validity, which compares a new test with an existing test that is already considered valid or relevant to see if they produce similar results, tested using the chi-squared or Fisher\u0026rsquo;s exact tests between existing PMS and PMDD screening tools (i.e., PSST revised for adolescents questionnaire) and the binary scores of each factor and total scores divided by the median. Predictive validity, which measures how well a test predicts outcomes in the future (i.e., whether the developed scale could predict absenteeism) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], we applied a logistic regression analysis and computed odds ratios with 95% confidence intervals. Multiple logistic regression was conducted with adjustments for previously burnout related covariates among Japanese working women [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] because burnout was considered as a proxy of absenteeism that eventually results in quitting a job: age, educational attainment, marital status, working status, and Copenhagen Burnout Inventory binary variables divided by the median. Finally, we drew receiver operating characteristic curves for absenteeism and estimated the area under the curve (AUC)s and cutoff points, with the optimal sensitivity and specificity based on the Youden Index.\u003c/p\u003e \u003cp\u003eAll analyses were performed using SAS (version 9.4, SAS Institute Inc., Cary, NC, USA) and Stata version 17 (Stata Corp., College Station, TX, USA). The significance level was two-sided and less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe characteristics of the 3,239 participants (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristics of 3239 participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN or Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% or SD\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\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBody Mass Index, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (missing n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\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 \u003ctd align=\"left\" colname=\"c4\"\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\u003e2251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.5\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.6\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\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAnnual household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003e\u0026gt;\u0026thinsp;8\u0026nbsp;million JPY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1\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\u003e6\u0026ndash;8\u0026nbsp;million JPY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\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\u003e4\u0026ndash;6\u0026nbsp;million JPY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8\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\u003e2\u0026ndash;4\u0026nbsp;million JPY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.3\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\u003e\u0026lt;\u0026thinsp;2\u0026nbsp;million JPY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eHigh/junior/elementary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.5\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\u003e2 year-college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8\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\u003eUniversity/Graduated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eClerical workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1\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\u003eService workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.9\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\u003eProfessional and engineering workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4\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\u003eSales workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\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\u003eManufacturing process workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\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\u003eAdministrative and managerial workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\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\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eMedical, Health Care and Welfare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\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\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3\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\u003eCompound Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.8\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\u003eWholesale and Retail trade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\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\u003eFinance and Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.9\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\u003eConstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\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\u003eEducation,Learning Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\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\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGynecological underlying illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eMyoma Uteri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.85\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\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.96\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\u003eOvarian cyst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59\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\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychological underlying illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\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\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.98\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe work characteristics of 3239 participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN or median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% or IQR\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\u003eWorking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eFull-time worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.0\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\u003ePart-time worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.9\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\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWorkplace size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e\u0026lt;\u0026thinsp;50 workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.3\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\u003e50\u0026ndash;100 workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.1\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\u003e100\u0026ndash;300 workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.0\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\u003e300\u0026ndash;1000 workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.3\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\u003e\u0026gt;\u0026thinsp;1000 workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLabor characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDaily average hours of working, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(7\u0026ndash;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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWeekly average hours of working, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(30\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDaily average hours of standing, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1\u0026ndash;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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNumbers of carrying heavy object, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNumbers of a night shift in previous month, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCopenhagen Burnout Inventory, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003ePersonal Burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25/100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.3\u0026ndash;50)\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\u003eWork related Burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.3/100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(25.0-53.6)\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\u003eClient related Burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.5/100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(20.8\u0026ndash;54.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWork productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEver been absent due to premenstrual symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.1\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.9\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDo not remember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHow long total absence at maximum (n\u0026thinsp;=\u0026thinsp;393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003emonths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReported n\u0026thinsp;=\u0026thinsp;106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1\u0026ndash;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\u003eweeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReported n\u0026thinsp;=\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1\u0026ndash;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\u003edays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReported n\u0026thinsp;=\u0026thinsp;216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1\u0026ndash;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\u003ehours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReported n\u0026thinsp;=\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1\u0026ndash;4)\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\u003eOur participants are female workers aged 18\u0026ndash;41 years (excluding pregnant and lactating women) in Japan. The average age was 32.6 years old. In terms of occupational type, the majority of the women were clerical workers (49%), followed by service workers (18%) and professional/technical workers (15%). In terms of industry, 18% of the participants were in the medical and welfare sectors, 14% were in the manufacturing sector, 11% were in the service sector, and 10% were in the wholesale and retail sectors. In terms of working status, 77% were full-time workers, with a median of 8 daily working hours. In terms of work productivity, 393 people, or 12%, had been absent due to premenstrual symptoms, with 106 people (27%) being absent for at least 1 year, 26 people (7%) being absent for 1 week, and 216 people (55%) being absent for 1 day.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eScale development: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA)\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the EFA and CFA [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The initial solution based on a scree plot with eigenvalues of 1.0 or greater suggested four factors. Using a split-half sample, we performed EFA, excluded items that were either cross-loaded or did not load enough to exceed 0.5, and repeated this process until we reached the lowest AIC in the CFA. This process eventually resulted in four domains and 27 items. We retained four factors based on the KMO measure (0.977) and Bartlett\u0026rsquo;s test below 0.05. The final model was investigated with the requirement of a well-fitted model (RMSEA\u0026thinsp;=\u0026thinsp;0.077, CFI\u0026thinsp;=\u0026thinsp;0.928, a TLI\u0026thinsp;=\u0026thinsp;0.921, SRMS\u0026thinsp;=\u0026thinsp;0.043, CD\u0026thinsp;=\u0026thinsp;1.000) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The domains included \"Autonomic dysfunction symptoms'' (13 items, Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.960, Macdonald\u0026rsquo;s omega\u0026thinsp;=\u0026thinsp;0.961), \"Psychiatric symptoms\" (6 items, Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.933, Macdonald\u0026rsquo;s omega\u0026thinsp;=\u0026thinsp;0.934), \"Lack of work efficiency\" (5 items, Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.947, Macdonald\u0026rsquo;s omega\u0026thinsp;=\u0026thinsp;0.948), and \u0026ldquo;Abdominal symptoms\u0026rdquo; (3 items, Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.821, Macdonald\u0026rsquo;s omega\u0026thinsp;=\u0026thinsp;0.827) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The \"Autonomic dysfunction symptoms\" had the highest factor loading, ranging between 0.528 and 0.892, followed by the \"Psychiatric symptoms,\" ranging between 0.590 and 0.884, and the \"Lack of work efficiency,\" ranging between 0.541 and 0.796, while the \"Abdominal symptoms\" had the smallest factor loadings, ranging between 0.604 and 0.703. The Cronbach\u0026rsquo;s α values for each factor exceeded 0.8, McDonald's omega exceeded 0.7, and the item correlations between each factor were reasonably high, suggesting good reliability and a high internal consistency for each factor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of Exploratory and Confirmatory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eExploratory Factor Analysis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1597; Maximum likelihood, Promax rotation)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFactor4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eFactor 1 \"Autonomic dysfunction\u003c/p\u003e \u003cp\u003esymptoms\", α\u0026thinsp;=\u0026thinsp;0.960, omega\u0026thinsp;=\u0026thinsp;0.961\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifficulty in breathing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.053\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\u003eDizziness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.238\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\u003eStumbling or falling over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.034\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\u003eNausea or vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.203\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\u003eHot flushes on face or upper body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\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\u003eChest pain or squeezing in the chest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.128\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\u003eTinnitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.033\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\u003eHeart beating or pounding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.019\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\u003eNumbness in the limbs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.047\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\u003eChilly lower back or limbs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.165\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\u003eBlurred vision or difficulty in seeing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.078\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\u003ePain in finger or knee joint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\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\u003eMuscle pain (clumps in the legs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 2 \"Psychiatric symptoms\", α\u0026thinsp;=\u0026thinsp;0.933, omega\u0026thinsp;=\u0026thinsp;0.934\u003c/b\u003e\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\u003eMood Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\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\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.136\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\u003eFeeling of extreme psychological instability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.017\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\u003eIrritability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.158\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\u003eLack of motivation to work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.097\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\u003eLack of concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 3 \"Lack of work efficiency\", α\u0026thinsp;=\u0026thinsp;0.947, omega\u0026thinsp;=\u0026thinsp;0.948\u003c/b\u003e\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\u003eExpending more time on routine work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.002\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\u003eFeeling of lower accomplishment at work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.007\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\u003eIncreased careless mistakes at work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.029\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\u003eExpending more time in finishing usual household chores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\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\u003eReduced working and learning ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 4 \"Abdominal symptoms\", α\u0026thinsp;=\u0026thinsp;0.821, omega\u0026thinsp;=\u0026thinsp;0. 827\u003c/b\u003e\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\u003eDiarrhea or constipation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.604\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\u003eAbdominal distention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.703\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\u003eAbdominal pain, cramps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor correlation (n\u0026thinsp;=\u0026thinsp;1597)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eFactor1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eFactor2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eFactor3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eFactor4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConfirmatory Factor Analysis Result (n\u0026thinsp;=\u0026thinsp;1642)\u003c/b\u003e\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\u003eRoot mean squared error of approximation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.077 (0.074\u0026ndash;0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eAkaike's information criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e79438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eComparative fit index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eTucker-Lewis index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eStandardized root mean squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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\u003eCoefficient of determination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eValidation of the developed scale: concurrent, criterion, and predictive validity\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the Spearman\u0026rsquo;s correlation coefficients between the developed scale and the Copenhagen Burnout Inventory scores [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We confirmed a high correlation with the three subscales of the Copenhagen Burnout Inventory (all \u003cem\u003eP\u003c/em\u003es\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting a high concurrent validity. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the criterion validity between the developed scale and PMS and PMDD based on the Premenstrual Symptoms Screening Tool (PSST) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Due to the small values for PMS and PMDD, we applied the chi-squared or Fisher\u0026rsquo;s exact tests and found that women who were in the upper half for each factor and the total score, were more likely to have PMS or PMDD (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, all \u003cem\u003eP\u003c/em\u003es\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This indicates that each factor in the scale developed had a high predictability of PMS or PMDD.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcurrent validity with Spearman correlation coefficient between the newly developed scale and the Copenhagen burnout score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e# of item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eCopenhagen Burnout Inventory\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePersonal Burnout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWork related Burnout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClient related Burnout\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 1 \"Autonomic dysfunction symptoms\"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (13\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.515*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.435*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 2 \"Psychiatric symptoms\"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (8\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.626*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.564*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.522*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 3 \" Lack of work efficiency \"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.545*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.472*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 4 \" Abdominal symptoms \"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.480*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.404*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.414*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (31\u0026ndash;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.628*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.548*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.517*\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCriterion validity between the developed scale and PMS and PMDD based on the Premenstrual Symptom Screening Tool\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003ePMS (n\u0026thinsp;=\u0026thinsp;331, 10%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003ePMDD (n\u0026thinsp;=\u0026thinsp;99, 3%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 1 \"Autonomic dysfunction symptoms\"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1692)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 2 \"Psychiatric symptoms\"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 3 \" Lack of work efficiency \"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e99.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1540)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 4 \" Abdominal symptoms\"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e*Based on Chi-square test or Fisher's exact test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the validation of the developed scale for work absenteeism by logistic regression models. Univariate associations using chi-squared testing or logistic regression models demonstrated that women who scored in the upper half for each factor and the total score were more likely to have ever had absenteeism from work. After adjusting for age, educational attainment, marital status, working status, and Copenhagen Burnout Inventory scores, women with who scored in the upper half for \u0026ldquo;Autonomic dysfunction symptoms\u0026rdquo; were 2.13-2.59-fold more likely, those with \u0026ldquo;Psychiatric symptoms\u0026rdquo; were 1.81-2.15-fold more likely, those with \"Lack of work efficiency\" were 1.96-2.36-fold more likely, and those with \" Abdominal symptoms \" were 2.05-2.56-fold more likely to have ever had absenteeism. Although OR of each factor in PBO, WBO, and CBO falls in each 95% CI, indicating no statistical difference, the OR of each factor is statistically associated with work absenteeism. Thus, the developed scale appeared to yield high predictive validity [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the logistic regression model validation of the absenteeism scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eWork absenteeism experience due to\u003c/p\u003e \u003cp\u003ePMS related symptoms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eLogistic regression models for Work absenteeism\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNo/Do not (n\u0026thinsp;=\u0026thinsp;2846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCrude odds ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eCopenhagen burnout inventory adjust odds ratio (95% CI)*\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePBO model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWBO model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCBO model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor1 \"Autonomic dysfunction symptoms \"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1692)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.32 (2.60\u0026ndash;4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003cp\u003e(1.88\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.59 (2.01\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.13 (1.67\u0026ndash;2.73)\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 2 \"Psychiatric symptoms\"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.44 (3.39\u0026ndash;5.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.09 (1.60\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.15 (1.65\u0026ndash;2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.81 (1.41\u0026ndash;2.33)\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 3 \"Lack of work efficiency\"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.85 (2.99\u0026ndash;4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.31 (1.78-3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.36 (1.82\u0026ndash;3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.96 (1.52\u0026ndash;2.51)\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1540)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor 4 \" Abdominal symptoms \"\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.32 (3.22\u0026ndash;5.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.47 (1.91\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.56 (1.99\u0026ndash;3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.05 (1.61\u0026ndash;2.62)\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\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\u003eUpper half (n\u0026thinsp;=\u0026thinsp;1627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.07 (3.90\u0026ndash;6.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.84 (1.40\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.99 (1.52\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.68 (1.30\u0026ndash;2.17)\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\u003eLower half (n\u0026thinsp;=\u0026thinsp;1612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e*Based on chi-square test. **Adjusted for age, educational attainment, marital status, working status, and Copenhagen burnout binary variable divided by median.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePredictive performance of the developed scale\u003c/h2\u003e \u003cp\u003eBased on the largest Youden index [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] to establish an optimal-thresholds, we estimated the total score of the disability index to be 40 points, with a sensitivity of 79%, specificity of 57%, positive predictive value of 20%, negative predictive value of 95%, positive likelihood ratio of 1.83, negative likelihood ratio of 0.36, and AUC of 0.735 (95% CI: 0.710\u0026ndash;0.760; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which were deemed acceptable after referencing the Hosmer and Lemeshow test [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eShort summary\u003c/h2\u003e \u003cp\u003eIn this study, we examined 3,239 female workers aged 18\u0026ndash;41 years (excluding pregnant and lactating women) in Japan as PMS is most prevalent in women in their late 20s to early 40s [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Of the initial 47 items we considered, the EFA identified four domains with 27 items, which was then determined to be a moderately fitted model using the CFA. The four domains included \"Autonomic dysfunction symptoms'' (13 items), \"Psychiatric symptoms\" (6 items), \"Lack of work efficiency\" (5 items), and \u0026ldquo;Abdominal symptoms\u0026rdquo; (3 items), with high Cronbach\u0026rsquo;s alphas and item correlations. In addition, the developed scale had a high concurrent validity, which refers to a high correlation with the three subscales of the Copenhagen Burnout Inventory [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and a high criterion validity due to significant associations with PMS or PMDD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Finally, we confirmed that the developed scale is capable of predicting work absenteeism caused by PMS-related symptoms, with an acceptable AUC. These results suggest that the scale developed in this study may be useful for helping working women to stay healthy and maximize their work productivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of the findings\u003c/h2\u003e \u003cp\u003eThe strength of this study is that we included a variety of symptoms both directly and indirectly related to PMS according to previous literature. Unfortunately, there have been no reference guidelines previously published for PMS. Also, PMDD has only recently been included in DSM-5 as a disease entity, with a heavy weighting on psychological symptoms [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Similarly, the PSST was previously developed by Steiner [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; however, physical symptoms were treated as only one of a total of 14 symptoms, of which 10 symptoms were psychological. In contrast, our scale is balanced and evenly divided into both physical (i.e., Autonomic dysfunction) and psychological factors, as well as the two other domains of a lack of work efficiency and abdominal symptoms. Our scale can be widely applied in the workplace to screen for potential absenteeism, which is very useful in terms of work productivity loss.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eFemale workers, corporations, and society need a comprehensive scale to evaluate PMS in the workplace. Therefore, our newly developed tool might be utilized as a scale for corporations to screen for female workers who struggle with PMS-related issues. This tool not only helps corporations identify and support female workers with PMS but also contributes to a more inclusive and supportive work environment. Existing scales like the DSM-5 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and PSST [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] are clinical questionnaires with a heavy weighting on psychological items; however, our developed scale is more balanced and practical for the workplace because it contains more comprehensive categories, including physical (i.e., Autonomic dysfunction) and psychological factors, a lack of work efficiency and abdominal symptoms. In addition, while the other clinical scales overlook work-related aspects for high-risk individuals, the new tool may detect female employees who are highly likely to be absent from work due to these symptoms. However, again, this scale is not meant to discriminate against women who suffer from menstrual symptoms but to screen women who might need further medical treatment. The scale measures the health status of women workers at the time of investigation but can be repeatedly used whenever a woman worker manifests menstrual symptoms. As this is a screening test, it is recommended that a positive result should be followed by a visit to a gynecologist or family doctor for a detailed diagnosis of PMS/PMDD and, if necessary, medication treatment. To summarize, the use of the developed scale promotes understanding of PMS within a company and might even lead to timely diagnosis of untreated underlying mental or gynecological illness (endometriosis, fibroids, etc.) that contributes to the possibility of increased work productivity. This scale, which was validated against existing PMS and PMDD scales, can help measure individual burdens in female workers suffering from PMS and can also be used by employers to assess women\u0026rsquo;s health status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eThere are several study limitations that should be examined and discussed. First, in this study, only 10% of women had PMS and 3% had PMDD, which was slightly less than the 20\u0026ndash;30% observed in community samples [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and the 5.9% of 303 healthy women aged 20\u0026ndash;45 years [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] assessed using the DSM-5 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which suggests that our study participants might have been healthier than these other populations. Second, according to a labor survey conducted by the Ministry of Internal Affairs and Communications in 2021, the most significant proportion of women (22%) are employed in the health and welfare industry, with the second most common industry being wholesale and retail trade at 17% [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, the proportion of individuals employed in the health and welfare industries was slightly smaller (18.5%), followed by manufacturing at 14.3%. The difference between our sample and whole women workers in Japan may not physically burden our participants since our study participants reported an average of only 8 hours per day and a median of only 2 hours of standing per day. This working condition might have influenced (reduced) the severity of PMS-associated symptoms [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Third, we only investigated the predictability of absenteeism because the definition clearly depends on the numbers of periods (i.e., days, months) due to menstrual symptoms compared to presenteeism which relies on self-reporting though it has been previously suggested that menstrual symptoms may affect presenteeism rather than absenteeism [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In this regard, we believe that this scale should not be used to discriminate in hiring processes. It would be important from an ethical perspective to address this concern. We only focused on absenteeism because of the financial losses incurred by individual companies, which can be justified in terms of corporate financial management. Fourth, in the previously developed PMS scale, PSST [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], physical symptoms were treated as only one of a total of 14 symptoms, of which 10 symptoms were psychological and the other three symptoms were related to a lack of motivation for work, household chores, or social interactions. In our study, as these three items are very similar, we replaced them with a single item devoted to motivation. Similarly, we employed one item instead of multiple similar items, such as four human relationships (irritated, troubled, disputed, and conflicted relationships) and three functional items (being unable to clean a room, washing, and cooking). Eventually, these items did not load further and were discarded. Fifth, a previous report [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] discussed how early-life stress influences the development of PMDD through hormonal and stress-response pathways. In our study, we asked for psychiatric history/diagnosis, including depression, but never asked for a history of childhood trauma, particularly emotional abuse and neglect, that might increase susceptibility for PMS/PMDD. Sixth, although our participants could potentially be of any nationality, the responses required at least a minimum level of Japanese language comprehension; therefore, we can assume that the majority of respondents were Japanese. Seventh, the scale developed in this study is not a diagnostic tool and, thus, would require medical evaluation to confirm or determine other causes of self-reported symptoms (i.e., PMDD or other psychiatric or gynecological illness). Finally, although our developed disability index is highly reliable and valid, the AUC was 0.7 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which suggests a moderate acceptability. The moderate level of AUC is explained by absenteeism, a form of work productivity to predict, which is usually a clinical outcome. Because the scale developed in this study is meant to identify PMS but not absenteeism, obtaining a high level of AUC may be difficult. Considering these limitations, our results should be interpreted with caution.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe developed a reliable and valid PMS screening tool for women workers. This scale would allow labor managers, business owners, and working women to detect PMS earlier. An appropriate intervention would resolve the health aspects of female workers, including their physical and psychological symptoms, work efficiency, and productivity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epremenstrual syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eexploratory factor analyses\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 analyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopenhagen Burnout Inventory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePMDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epremenstrual dysphoric disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePMTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePremenstrual Tension Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePremenstrual Assessment Form\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePremenstrual Symptoms Screening Tool\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDRSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDaily Record of Severity of Problems\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMenstrual Distress Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWPAQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWork-related Physical Activity Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaiser\u0026ndash;Meyer\u0026ndash;Olkin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eroot-mean-square error of approximation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative fit index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTucker\u0026ndash;Lewis Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoefficient of determination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to all the participants in this research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design:\u0026nbsp;KN,\u0026nbsp;CT; Collection of data:\u0026nbsp;KN,\u0026nbsp;EM, CT; Analysis and interpretation of data:\u0026nbsp;CT, KS, MI, FT, EM, SJ, and KN; Revision of the paper:\u0026nbsp;CT, KS, MI, FT, EM, SJ, and KN.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Ministry of Education, Culture, Sports, Science and Technology, Japan (Grants for Scientific Research [B], Number 21H03192), and Akita University Graduate School of Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Akita University (No. 2712, approval date, July 7th, 2021). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\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\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted without any commercial or financial relationships that could potentially create a conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWomen\u0026rsquo;s Health Initiatives in Health Management. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.meti.go.jp/policy/mono_info_service/healthcare/downloadfiles/josei-kenkou.pdf\u003c/span\u003e\u003cspan address=\"https://www.meti.go.jp/policy/mono_info_service/healthcare/downloadfiles/josei-kenkou.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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BMC Med Educ. 2020;20(1):178.10.1186/s12909-020-02072-x.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen F, Xue Y, Tan MT, Chen P. Efficient statistical tests to compare Youden index: accounting for contingency correlation. Stat Med. 2015;34(9):1560\u0026thinsp;\u0026ndash;\u0026thinsp;76.10.1002/sim.6432.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression: Wiley; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eACOG-Clinical. Management of Premenstrual Disorders: ACOG Clinical Practice Guideline No. 7. Obstet Gynecol. 2023;142(6):1516\u0026thinsp;\u0026ndash;\u0026thinsp;33.10.1097/aog.0000000000005426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHantsoo L, Epperson CN. Premenstrual Dysphoric Disorder: Epidemiology and Treatment. Curr Psychiatry Rep. 2015;17(11):87.10.1007/s11920-015-0628-3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaik SS, Nidhi Y, Kumar K, Grover S. Diagnostic validity of premenstrual dysphoric disorder: revisited. Front Glob Womens Health. 2023;4:1181583.10.3389/fgwh.2023.1181583.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association, Association D. AP. Diagnostic and statistical manual of mental disorders: DSM-5. American psychiatric association Washington, DC; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnnual Report of the Labor Force Survey. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stat.go.jp/data/roudou/report/2021/index.html\u003c/span\u003e\u003cspan address=\"https://www.stat.go.jp/data/roudou/report/2021/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 3 November 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohebbi Dehnavi Z, Jafarnejad F, Sadeghi Goghary S. The effect of 8 weeks aerobic exercise on severity of physical symptoms of premenstrual syndrome: a clinical trial study. BMC Womens Health. 2018;18(1):80.10.1186/s12905-018-0565-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUchibori M, Eguchi A, Ghaznavi C, Tanoue Y, Ueta M, Sassa M et al. Understanding factors related to healthcare avoidance for menstrual disorders and menopausal symptoms: A cross-sectional study among women in Japan. Prev Med Rep. 2023;36:102467.10.1016/j.pmedr.2023.102467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoetzel RZ, Long SR, Ozminkowski RJ, Hawkins K, Wang S, Lynch W. Health, absence, disability, and presenteeism cost estimates of certain physical and mental health conditions affecting U.S. employers. J Occup Environ Med. 2004;46(4):398-412.10.1097/01.jom.0000121151.40413.bd.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei S, Tang YY, Wang F, Wang Y, Geng X, Editorial. Neural circuits and neuroendocrine mechanisms of depression and premenstrual dysphoric disorder: towards precise targets for translational medicine and drug development, volume II. Front Psychiatry. 2023;14:1216689.10.3389/fpsyt.2023.1216689.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Absenteeism, Burnout, Female workers, Premenstrual Syndrome, Scale development","lastPublishedDoi":"10.21203/rs.3.rs-6644419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6644419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eWe aimed to develop a new screening tool for premenstrual syndrome (PMS) to be used in the workplace.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn October 2021, we recruited 3,239 working women with menstruation via an internet research company and asked 47 questions about PMS-related symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the participants, 331 women had experienced PMS (10%), and 393 women had taken sick leave because of PMS-associated symptoms (12%). Explanatory factor analyses with maximum likelihood and Promax rotation identified four domains with 27 items, including \"Autonomic dysfunction symptoms'' (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.93), \"Psychiatric symptoms\" (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.94), \"Lack of work efficiency\" (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.93), and \u0026ldquo;Abdominal symptoms\u0026rdquo; (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.95). Using a split-half sample for the confirmatory factor analysis, moderately fit model indices for the four-factor solution were confirmed. We also confirmed the developed scale\u0026rsquo;s criterion validity using existing PMS screening criteria and its concurrent validity through high correlation coefficients with Copenhagen Burnout Inventory scores. The receiver operating characteristic curve yielded a good predictive ability for work absenteeism, including a sensitivity of 78%, a specificity of 57%, and an area under the curve of 0.735.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA highly reliable and valid new scale for PMS was developed with efficacy for screening for work absenteeism.\u003c/p\u003e","manuscriptTitle":"Development of a premenstrual syndrome scale for working women and its validation against work productivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 15:38:52","doi":"10.21203/rs.3.rs-6644419/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-07T21:49:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-31T04:03:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63378735958068050449133784251349347579","date":"2025-07-23T06:10:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T11:19:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311538314507034602233608273937400036944","date":"2025-07-19T20:38:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215285664465176461414100497745848889534","date":"2025-07-18T09:57:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-05T04:18:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T08:49:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178061916180174179819879563915328538144","date":"2025-06-23T08:19:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19852207819835011982072688346446662112","date":"2025-06-18T23:43:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T04:37:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-13T07:36:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T05:16:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-13T05:14:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-05-12T08:11:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec4e6163-3ed0-40cb-8048-44b4dc879420","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:02:17+00:00","versionOfRecord":{"articleIdentity":"rs-6644419","link":"https://doi.org/10.1186/s12905-025-04092-5","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-11-07 15:57:45","publishedOnDateReadable":"November 7th, 2025"},"versionCreatedAt":"2025-06-10 15:38:52","video":"","vorDoi":"10.1186/s12905-025-04092-5","vorDoiUrl":"https://doi.org/10.1186/s12905-025-04092-5","workflowStages":[]},"version":"v1","identity":"rs-6644419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6644419","identity":"rs-6644419","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}