Medicine®

Observational Study

Diagnostic model for preschool workers’
unwillingness to continue working
Developed using machine-learning techniques
Moemi Matsuo, PhDa , Koutarou Matsumoto, PhDb,*, Misako Higashijima, PhDa, Susumu Shirabe, PhDc,
Goro Tanaka, PhDd,e, Yuri Yoshida, PhDe,f, Toshio Higashi, PhDd, Hiroya Miyabara, PhDa, Youhei Komatsu, PhDa,
Ryoichiro Iwanaga, PhDd,eAbstract

The turnover of kindergarten teachers has drastically increased in the past 10 years. Reducing the turnover rates among preschool
workers has become an important issue worldwide. Parents have avoided enrolling children in preschools due to insufficientcare, which affects their ability to work. Therefore, this study developed a diagnostic model to understand preschool workers’
unwillingness to continue working. A total of 1002 full-time preschool workers were divided into 2 groups. Predictors were drawn
from general questionnaires, including those for mental health. We compared 3 algorithms: the least absolute shrinkage andselection operator, eXtreme Gradient Boosting, and logistic regression. Additionally, the SHapley Additive exPlanation was used tovisualize the relationship between years of work experience and intention to continue working. The logistic regression model was
adopted as the diagnostic model, and the predictors were “not living with children,” “human relation problems with boss,” “highrisk of mental distress,” and “work experience.” The developed risk score and the optimal cutoff value were 14 points. By using
the diagnostic model to determine workers’ unwillingness to continue working, supervisors can intervene with workers who areexperiencing difficulties at work and can help resolve their problems.
Abbreviations: AUROC = area under the receiver operating characteristic curve, K6 = Kessler Psychological distress scale,

LASSO = least absolute shrinkage and selection operator regression, LR = logistic regression, ML = machine-learning, SHAP =SHapley additive exPlanations, XGBoost = eXtreme gradient boosting.
Keywords: diagnostic model, machine-learning (ML), mental health, turnover, work environment

A study suggested that “older age,” “living with a spouse,”“responsibilities related to care of younger children at work,”“better mental health,” and “higher work engagement” weresignificantly associated with a higher willingness to continue
working.[7] It is believed that welfare benefits, individual support systems, balanced work conditions, and workers’ highagreement with workplace childcare/education policies may
be associated with reduced turnover.[7] Moreover, other studies
suggest that early childhood teachers who are willing to continue working for >5 years provide significantly higher quality programs than those who are not willing to work for that
length of time.[8] Taken together, empirical research has thus
identified some key factors associated with reduced turnover(e.g., welfare benefits, individual support systems, balanced
work conditions, and workers’ high agreement with workplace

1. Introduction“Teacher turnover” is an umbrella term that refers to teachers
who leave the teaching profession or move to another school.[1]In the past decades, a dramatic increase has been noted in the
proportion of kindergarten teachers leaving the profession ortransferring to other schools. The estimated cost of teacher
turnover in public schools is over $7.3 billion a year in theUS.[2] The importance of reducing turnover rate among preschool workers is increasing in developed countries,[3,4] including in East Asia.[5] For instance, Japan has been experiencing ashortage of kindergarten teachers.[6] Therefore, kindergartens
provide insufficient childcare, causing parents to avoid enrolling their children. This situation may affect parents’ ability to
work.

* Correspondence: Koutarou Matsumoto, Biostatistics Center, Kurume University,
67 Asahimachi, Kurume, Fukuoka 830-0011, Japan (e-mail: matsumoto_koutarou@kurume-u.ac.jp).

The authors have no funding to disclose.
The authors have no conflicts of interest to disclose.

Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.
This is an open-access article distributed under the terms of the Creative Commons
Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible todownload, share, remix, transform, and buildup the work provided it is properly
cited. The work cannot be used commercially without permission from the journal.

The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request.
Supplemental Digital Content is available for this article.
Faculty of Rehabilitation Sciences, Nishi Kyushu University, Kanzaki, Saga,Japan, b Biostatistics Center, Kurume University, Kurume, Fukuoka, Japan,cNational Research Center for the Control and Prevention of Infectious
Diseases Nagasaki University, Nagasaki, Japan, d Unit of Medical Science,
Nagasaki University Graduate School of Biomedical Sciences, Nagasaki,
Japan, e Center for Child Mental Health Care and Education, Nagasaki
University, Nagasaki, Japan, f Faculty of Education, Nagasaki University,
Nagasaki, Japan.
aHow to cite this article: Matsuo M, Matsumoto K, Higashijima M, Shirabe S,
Tanaka G, Yoshida Y, Higashi T, Miyabara H, Komatsu Y, Iwanaga R. Diagnostic
model for preschool workers’ unwillingness to continue working: Developed using
machine-learning techniques. Medicine 2023;102:2(e32630).
Received: 17 November 2022 / Received in final form: 20 December 2022 /Accepted: 21 December 2022
http://dx.doi.org/10.1097/MD.0000000000032630

1

Matsuo et al. • Medicine (2023) 102:2Medicine

Consequently, the participants were divided into 2 groups:
those willing to continue working for ≥5 years and thosewilling to continue working for <5 years. All questionnaire
responses were self-reported, anonymous, and returned insealed envelopes. The questionnaire took approximately 10
minutes to complete.

childcare/education policies). However, a diagnostic model that
calculates turnover rates is yet to be formulated. Therefore,
this study develops a diagnostic model to analyze the unwillingness to continue working among preschool workers using
machine-learning (ML) models. The models developed using
ML techniques can be expected to predict turnover with a highdegree of accuracy. The findings will help preschool administrations develop retention and replacement strategies by accurately diagnosing unwillingness to continue working in theirfaculty members.

2.4. Predictors
2.4.1. Demographic questionnaire. A general questionnaire
was administered, which comprised 13 sections: gender, age,family environment, educational background, work experience,
main responsibilities at work, type of workplace, current annual
salary, desired salary, sum of overtime work for the last 30 days,
sources of human relations problems, distressing aspects ofchildcare, and willingness to continue working in the present
position for ≥5 years. Table 1 reports the comparative data fromthis questionnaire between participants of the 2 aforementioned
groups.

2. Methodology
2.1. Study design
This was a cross-sectional study. The participants were recruitedfrom several kindergartens (14 public kindergartens and 32 private kindergartens), authorized childcare institutions (8 public authorized childcare institutions and 44 private authorizedchildcare institutions), nursery centers (22 public nursery centers and 13 private nursery centers) across Nagasaki, Omura,and Goto in Japan. We developed the diagnostic models inaccordance with the Transparent Reporting of a multivariable diagnostic model for Individual Prognosis or Diagnosis
statement.

2.4.2. Mental health. The Kessler Psychological Distress Scale(K6) was used to assess the mental health of preschool workers.
The scale has been employed in annual government healthsurveys in the US, Canada, and the World Health Organization
World Mental Health Surveys.[9] The scale assesses the frequencyof experiencing psychological distress over a period of 30days using 6 items. Participants responded to the items on a
5-point Likert scale (0 = not at all, 1 = rarely, 2 = sometimes,
3 = often, 4 = always). High K6 scores indicate a high level ofpsychological distress. The participants were categorized into
2 groups based on their scores: K6 < 13 or K6 ≥ 13. Scores in
the K6 ≥ 13 category indicated that the participants frequently
faced psychological distress.

2.2. Participants
A total of 5415 questionnaires were sent to preschool workers
in 2018, and 2304 (43%) of the distributed questionnaires werereturned. Of these, 773 questionnaires with part-timers, 185with those in managerial positions, and 344 with no responseto the item about their willingness to continue working were
excluded. From the rest of 1002 questionnaires, a total of 451
questionnaires with missing data were excluded for analysis(Fig. 1), and the remaining 551 questionnaires were used todevelop the diagnostic model.
The participants provided informed consent in their workplaces. The study was approved by the Ethics Committee of
Nishi Kyushu University (approval No. 21VDV15) and complied with the Declaration of Helsinki.

2.4.3. Missing data. The rates of missing values for eachvariable used in the diagnostic model are shown in Table S1,
Supplemental Digital Content, http://links.lww.com/MD/I316.
The highest proportion of missing data was for the item assessing
the sum of overtime work for the last 30 days, with 2 response
options: overtime hours at the workplace (missing data n = 264)
and overtime hours at home (n = 294). In the sensitivity analysis,
to confirm the effect of the exclusion of missing values on the
diagnosis results, we developed a diagnostic model by excluding
variables with many missing values from the questionnaire.
A flow chart of participant selection is shown in Figure S1,
Supplemental Digital Content, http://links.lww.com/MD/I310.

2.3. Outcome
Unwillingness to continue working was defined as the main
outcome variable. The original questionnaire was used tomeasure the outcome (i.e., willing to continue working
for ≥5 years or willing to continue working for <5 years).

2.5. Statistical analysis
We compared differences in the baseline variables using theχ2 test, Fisher exact test, or Mann–Whitney U test, as appropriate. To develop the diagnostic model, we compared the
diagnostic performance of 3 algorithms: 1 logistic model and
2 ML models. For the logistic model, variable selection was
done using the backward method using Akaike Information
Criterion. For the 2 ML models, we used the regularization
method (the least absolute shrinkage and selection operator
regression model)[10] and the ensemble decision tree model(eXtreme Gradient Boosting [XGBoost]).[11] Details of the
ML programs are shown in the supplementary material, (seeSupplementary Data – R programs, Supplemental DigitalContent, http://links.lww.com/MD/I319, which explain thecontents of the R-program).
The discriminating ability of each model was assessed by thearea under the receiver operating characteristic curve (AUROC),
sensitivity, and specificity. The calibration performance of eachmodel was assessed using the calibration plot. We performed astratified 5-fold cross-validation for internal validation (Figure S2,
Supplemental Digital Content, http://links.lww.com/MD/I311).

Figure 1. Flow chart of the research. The 551 participants who met the
research criteria were divided into 2 groups. The first group included workers
who were willing to continue working for ≥5 years, and the second group
included those who were willing to continue working for <5 years.

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Table 1Comparison of variables between workers’ willingness to continue working for ≥5 years and <5 years.
Variables

Willingness to continue working for ≥5 yr n = 256

Gender, n (%) Female MaleAge (in yr), n (%) 20–29 30–39 40–49 >50
Family environment, n (%) Living alone/Transfer without family Living with a spouse Living with children Living with parent Living with spouse’s parent Living with brother/sister Living with others
Educational background, n (%) Lower than bachelor’s degree Bachelor’s degree/or higher
Work experience (yr)Main responsibilities at work, n (%) 0–2 yr old childcare or other 3–5 yr old childcare or education
Type of workplace, n (%) Kindergarten Authorized childcare institution Nursery center Others
Mental health K6 < 13, n (%) K6 ≥ 13, n (%)
Current annual salary <2 million yen 2 million yen–3 million yen 3 million yen–4 million yen 4 million yen–5 million yen ≤5 million yen
Desired salary <2 million yen 2 million yen–3 million yen 3 million yen–4 million yen 4 million yen–5 million yen ≤5 million yen
Sum of overtime work for the last 30 d
 At workplace (h) At home (h)Cause of human relation problems, n (%) Boss Colleagues Subordinates Guardians Children Others
Distressing aspects of childcare, n (%) Nothing Children’s behavioral problems Children’s human relations problems Children’s emotional breakdowns Governance of whole classes

Willingness to continue working for <5 yr n = 295

P value251 (98)5 (2)290 (98)
5 (2)

1.00

85 (33)62 (24)57 (22)52 (20)191 (65)66 (22)22 (7)
16 (5)<.01*

43 (17)109 (43)
108 (42)91 (36)9 (4)36 (14)17 (7)

35 (2)
52 (18)38 (13)193 (65)7 (2)71 (24)18 (6)

.13
<.01*
<.01*
<.01*
.59
<.01*
.93

191 (75)
65 (25)
12 (4–22)

174 (59)
121 (41)
5 (2–10)<.01*134 (52)
122 (48)

92 (31)203 (69)<.01*

46 (18)52 (20)156 (61)2 (1)

74 (25)84 (28)129 (44)
8 (3)

<.01*

235 (92)
21 (8)

220 (75)
75 (25)<.01*

33 (13)106 (41)
81 (32)26 (10)10 (4)

64 (22)170 (58)
54 (18)
4 (1)
3 (1)

<.01*

4 (2)
44 (17)89 (35)78 (30)41 (16)7 (2)76 (26)124 (42)65 (22)23 (8)<.01*

10 (3–20)
6 (2–20)

15 (8–40)
10 (3–30)

<.01*
.02*

51 (20)71 (28)20 (8)
70 (27)22 (9)7 (3)137 (46)
88 (30)26 (9)
79 (27)47 (16)
1 (0)

<.01*
.65
.79
.96
.01*
.03*

71 (28)97 (38)32 (13)68 (27)32 (13)54 (18)136 (46)
71 (24)95 (32)58 (20)

.01*
.06
<.01*
.18
.03*

<.01*

Data are expressed as median (interquartile range), or number (%).
K6 = Kessler Screening Scale for Psychological Distress.
*P < .05, χ2 test or Mann–Whitney U test or Fisher exact test.

In addition, we used the SHapley Additive exPlanations(SHAP), interpretability method of ML, to visualize the relationship between years of work experience and willingness tocontinue working, as “work experience” was not an important
predictor in the logistic regression (LR) model but was in the

XGBoost model. SHAP is an excellent method for visualizing the contribution of predictors by applying Shapley values,
which are guaranteed to be fairly distributed in cooperative
game theory.[12] The XGBoost model was used to calculate the
SHAP values.
3Matsuo et al. • Medicine (2023) 102:2Medicine

experience” was identified as an important predictor in the MLmodels but not in the LR model. Similar results were obtainedin the sensitivity analysis (Figure S6, Supplemental DigitalContent, http://links.lww.com/MD/I315).

The variable importance of ML models, the variables selected bybackward method of the LR model, and the results of SHAP wereused to narrow down the predictors for the final diagnostic model(Figure S3, Supplemental Digital Content, http://links.lww.com/
MD/I312). Finally, the model was developed using an LR modeland scored based on the standardized regression coefficients.
Two-sided P values <.05 were considered statistically significant. All statistical analyses were performed using the R statistical software (Version 4.1.0).

3.4. Visualization using SHAP
We further performed SHAP analysis, as “work experience”
was an important predictor in the XGBoost model. Although
the risk of unwillingness to continue working was low among
newly employed participants, the risk increased from the first to
the tenth year in the job. Furthermore, the risk of unwillingness
to continue working decreased after the tenth year of employment and saturated after the twentieth (Fig. 4).

3. Results
3.1. Participants and baseline variables
The data of 551 analysis subjects were used to develop the diagnostic model. In total, 256 participants reported being willing towork for ≥5 years. Among these, 251 were women and 5 weremen; 33%, 24%, 22%, and 20% were in their 20s, 30s, 40s, and>50, respectively. Meanwhile, 295 participants reported being
willing to continue working for <5 years (290 women and 5
men; 65%, 22%, 7%, and 5% were in their 20s, 30s, 40s, and>50, respectively). First, we investigated the differences between
the 2 groups in terms of baseline variables. As shown in Table 1,
the 2 groups significantly differed on several variables, such as
age and family environment. Moreover, the variable scores of the
included and excluded participants were also compared; the values for 15 variables were significantly different (P < .01–< .05;
Table S2, Supplemental Digital Content, http://links.lww.com/
MD/I317).

3.5. Developing the final model
We developed 3 LR models by referring to the important variables identified in the ML models and the variables selectedin the backward method of the LR model and SHAP analysis.
Model 1 employed the 3 variables “human relation problems
with boss,” “not living with children,” and “mental health;K6 ≥ 13,” which were identified as important predictors in all
the diagnostic models. Models 2 and 3 added “work experience”

3.2. Discrimination and calibration of each diagnosticmodel
We evaluated AUROCs in diagnosing unwillingness to workthrough ML models using least absolute shrinkage and selection operator regression or XGBoost, and the LR model with all
available data. The ML models demonstrated a higher AUROC
than LR, albeit by a small margin (Table 2 and Fig. 2). Regarding
the calibration of each model, we plotted observed probability against diagnosed probability for the outcome. Calibration
plots showed general concordance with some exceptions
between diagnosed and observed probabilities in all the models (Figure S4, Supplemental Digital Content, http://links.lww.
com/MD/I313). In the sensitivity analysis, the discriminationand calibration performances were also evaluated in the samemanner; the results were similar to the main analysis (Table S3,
Supplemental Digital Content, http://links.lww.com/MD/I318
and Figure S5, Supplemental Digital Content, http://links.lww.
com/MD/I314).
Figure 2. Sensitivity/1-specificity curve. The ROC curves for the 3 diagnostic
models are shown. The black curve indicates the result of XGBoost. The red
curve indicates the result of LASSO. The green curve indicates the resultof LR. AUROC = the receiver operating characteristic (ROC) curve, LASSO= least absolute shrinkage and selection operator regression, LR = logistic
regression, SHAP = SHapley Additive exPlanations, XGBoost = eXtreme
Gradient Boosting.

3.3. Predictors for the outcome
Thereafter, we evaluated the predictors that contributed to thediagnosis within each of the diagnostic models (Fig. 3). The
variables “human relation problems with boss,” “not livingwith children,” and “mental health; K6 ≥ 13” were identified
as important predictors across all diagnostic models. “Work

Table 2Discriminative ability of diagnostic models.

LR modelLASSO model
XGBoost model

AUROC

Sensitivity

Specificity.755 ± .042
.760 ± .038
.769 ± .033

.705 ± .076
.770 ± .088
.793 ± .094

.719 ± .084
.683 ± .076
.679 ± .158

The mean and standard deviation of each metric after stratified 5-fold cross-validation are shown.
AUROC = area under the receiver operating characteristic curve, LASSO = least absolute shrinkage and selection operator regression, LR = logistic regression, XGBoost = eXtreme Gradient Boosting.

4Matsuo et al. • Medicine (2023) 102:2www.md-journal.com

Figure 3. Variable importance of each diagnostic model. (A) Indicating variable importance by XGBoost. (B) Indicating variable importance by LASSO. (c)Indicating the number of times the predictors were selected and significant in the backward method of LR. LASSO = least absolute shrinkage and selection
operator regression, LR = logistic regression, XGBoost = eXtreme Gradient Boosting.

Figure 4. SHAP dependence plot for willingness to continue working and work experience. Each dot is a person. The x-axis is their work experience, andthe y-axis is the SHAP value attributed to their work experience. Higher SHAP values represent higher risk of unwillingness to continue working due to work
experience. SHAP = SHapley Additive exPlanations.

5Matsuo et al. • Medicine (2023) 102:2Medicine

to the above 3 predictors, with Model 3 further classifying thevalue of “work experience” by referring to the SHAP analysis
results. The discrimination performance of Model 3 was the best
among the 3 LR models (Table 3).
Finally, we developed a risk score, referring to the standardized partial regression coefficients of Model 3 (Table 4). ROC
analysis (Youden index method) was performed using the developed risk score and the optimal cutoff value was 14 points(Fig. 5).

the importance of work conditions such as salary and workload
have also been suggested.[13,18,19] In this study, “not living withchildren,” “human relation problems with boss,” and “high riskof mental distress” were important predictors of unwillingness
to continue working, whereas the variables of “gender,” “age,”

Table 4Standardized regression coefficients for the final LR model andrisk score points.

4. Discussion

Standardized regression
coefficients

Characteristic

This study developed a diagnostic model to analyze the unwillingness to continue working among preschool workers. The
major findings of this study were as follows. All the diagnostic
models showed generally good discrimination and calibration
performance. The ML diagnostic models performed better thanthe LR model; however, the difference was not much. SHAP
analysis using the XGBoost model revealed a non-linear relationship between work experience and unwillingness to continue working. Thereafter, a risk score was developed using an
LR model after adding categorized job experience, considering
its non-linear relationship as a predictor. In addition, similar
predictive performance and factors were observed in the sensitivity analysis, suggesting that the influence of bias due to missing data is low.
Although models that diagnose unwillingness to continue
working have been developed,[13–15] and some counterplans
were suggested,[16] diagnostic models using ML have not. For
the first time in our study, we used ML to develop a model todiagnose the unwillingness to continue working in preschool
workers. However, contrary to our intention, we were unable
to develop high-performing models; furthermore, there wereminimal differences in the performance of the ML models
compared to the LR model. The XGBoost model, which we
expected to have a particularly high diagnostic performance,
is a type of decision tree ensemble model that has desirable
features, such as the ability to automatically handle non-linear relationships and interactions and missing values as
they are. However, due to internal processing complexities,
overfitting may occur when the number of data or eventsis small. Therefore, we focused on the advantage of capturing the non-linear relationship of the decision tree ensemble
learning models (XGBoost, etc.) and visualized it using theSHAP method. Consequently, we found a non-linear relationship between job experience and high turnover intentions.
Nevertheless, an LR model has become the gold standard for
calculating risk scores because of its ease of interpretation andgood diagnosis performance. In the future, this new method of
calculating risk scores, which combines the advantages of ML
methods with previously used methods, is expected to become
the gold standard.
Multiple previous studies have discussed the predictors associated with the willingness to continue working. In some studies, the importance of psychological predictors such as mental
health and human relations have been suggested.[5,17] In addition,

Not living with children
K6 ≥ 13
Human relation problems with boss
>20 yr of work experience
11–20 yr of work experience
2–10 yr of work experience
0–1 yr of work experience
Total

Score

.576
.465
.427
Reference
.519
.876
.45

6
5
4
0
5
9
5
34

Risk scores were generated by multiplying the standardized regression coefficient by 10 androunding.
K6 = Kessler Screening Scale for Psychological Distress score, LR = logistic regression.

Figure 5. ROC curve and cut-off value for risk score. The results of the ROC
curve for the risk score are shown. The optimal cutoff value using the Youden
index method was 14 points. ROC = receiver operating characteristic.

Table 3Discriminative ability of LR models built with variables of high importance.

LR Model 1
LR Model 2
LR Model 3
LR Model 3

AUROC

Sensitivity.741 ± .029
.763 ± .054
.774 ± .050
.774 ± .050

.685 ± .196
.851 ± .089
.790 ± .121
.790 ± .121

Specificity.700 ± .226
.599 ± .161
.654 ± .190
.654 ± .190

Model 1: not living with children, K6 ≥ 13, human relation problems with boss. Model 2: Model 1 + years of work experience. Model 3: Model 2 + categorized years of work experience; the mean andstandard deviation of each metric after stratified 5-fold cross-validation are shown.
AUROC = area under the receiver operating characteristic curve, LR = logistic regression.

6

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developed by integrating ML techniques with an LR model.
Using the diagnostic model, supervisors can intervene for workers with difficulties at work and resolve their problems. The
findings of this study will contribute to the prevention of worker
turnover intention among preschool workers.

“educational background,” “main responsibilities at work,”“type of workplace,” and “salary” were not significantly correlated with willingness to continue working. Additionally, similar to Tayama et al,[6] we found that “work experience” also
influenced unwillingness to continue working.
Generally, it is difficult for workers with children to leave theworkforce, as they need the financial resources to meet theirchild-rearing responsibilities. Although many teachers reportthat one of the challenges to continuing working involves taking care of their own children, the current study suggests thatworkers without children are more likely to leave work. In
recent years, gender equality has been promoted and employers
are focused on supporting workers with children by providing
family-friendly work conditions. Such work conditions could
increase workers’ satisfaction levels.[9,18] Conversely, under thesework conditions, workers without children must work harder
to make up for the absence of workers on maternal or paternal
leave, with the same reward conditions (i.e., salary) in Japan.
This may lead to increasing dissatisfaction among workers
without children. These findings suggest that we should pay
more attention to the actual reasons that workers without children are unwilling to continue working.
Studies have reported that human relation problems and difficulty caring for children were associated with a high risk of mental health issues among preschool workers,[8] whereas healthyinterpersonal relationships can improve mental health.[20,21]
Thus, better human relations may relate to better mental health.
Moreover, programs such as relationship counseling could positively impact teachers’ mental health.[20,21]
A cross-sectional study suggested that better mental healthwas significantly associated with a higher willingness to continue working.[22] Higher job satisfaction positively affects mental health, and higher job satisfaction and work engagement
have been shown to lower turnover rates.[4,23–26] Another study
suggested that better implementation of stress management
plans by the government could have a beneficial impact on the
broader psychosocial needs of workers.[27]Although workers with more work experience have lessrole stress and are more satisfied,[28] the importance of work
experience has not been discussed thoroughly in the literature.
We found that the risk of unwillingness to continue working
was low among newer employees; however, it increased fromthe first year to the tenth year in the job. Notably, the risk of
unwillingness to continue working decreased after the tenthyear of employment and saturated after the twentieth. Thesefindings could reflect various aspects of workers’ motivation;for instance, new employees have high motivation to work, but,over the years, they begin to face problems. Then, around thetenth year of employment, workers’ motivation might change,
especially if they get married or have children. After the twentieth year of employment, they might not consider leaving work
because in Japanese society it is difficult to change workplaces
as one ages. However, our findings warrant further research tocomprehensively examine this predictor.
This study has several limitations. First, the participant
selection may have led to biases, as all the participants wererecruited from a single prefecture in Japan. Second, there were
missing data in the baseline variables, which may have also led
to selection bias. Third, the possibility of overfitting cannot
be completely excluded, although diagnosis models constitutedby the training set were fitted to the test set. In future research,
the generalizability of our findings should be assessed in othersocial settings and other countries. Fourth, we did not collectdata on how many children were enrolled in the participating institutions; the ratio of workers to children could not becalculated.
In developing a diagnostic model to predict one’s willingness to continue working, “not living with children,” “humanrelations problems with boss,” “K6 ≥ 13,” and “work experience” were identified as important predictors. The model was

Acknowledgments
The authors would like to extend their gratitude to the participants of this study.

Author contributions
MM contributed to the research concept, design, definition of
intellectual content, literature search, data analysis, manuscript
preparation, editing, and review. KM contributed to the definition of intellectual content, statistical analysis, and manuscript
editing. MH, SS, GT, YY, TH, HM, and YK contributed to definition of intellectual content and manuscript review. RI contributed to the research concept, design, definition of intellectual
content, manuscript editing, and review.
Conceptualization: Moemi Matsuo, Koutarou Matsumoto,
Misako Higashijima, Susumu Shirabe, Goro Tanaka, Yuri
Yoshida, Toshio Higashi, Hiroya Miyabara, Youhei Komatsu,
Ryoichiro Iwanaga.
Data curation: Moemi Matsuo, Koutarou Matsumoto,
Ryoichiro Iwanaga.
Formal analysis: Moemi Matsuo, Koutarou Matsumoto.
Investigation: Ryoichiro Iwanaga.
Methodology: Moemi Matsuo, Ryoichiro Iwanaga.
Project administration: Moemi Matsuo.
Supervision: Moemi Matsuo.
Validation: Moemi Matsuo.
Visualization: Moemi Matsuo
Writing – original draft: Moemi Matsuo.
Writing – review & editing: Moemi Matsuo, Koutarou
Matsumoto, Misako Higashijima, Susumu Shirabe, Goro
Tanaka, Yuri Yoshida, Toshio Higashi, Hiroya Miyabara,
Youhei Komatsu, Ryoichiro Iwanaga.

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