JP7261441B2 - Program, judgment system and judgment device for judging working conditions - Google Patents

Description

The present invention relates to a program, a determination system, and a determination device for determining working conditions.

Conventionally, the turnover of workers such as nurses has been viewed as a problem. Burnout is one of the causes of job turnover, and various research reports have been made. Non-Patent Document 1 is an example of this, and examines burnout, stress, and intention to leave a job in Japanese nurses. Non-Patent Document 1 discusses the relationship between various factors including stress and burnout. In addition, the Ministry of Health, Labor and Welfare has introduced a stress check system based on the Industrial Safety and Health Act, mainly for the purpose of preventing mental health problems in workers.

Takashi Ohue, 2 others, "Examination of a cognitive model of stress, burnout, and intention to resign for Japanese nurses", Japan Journal of Nursing Science, 2011, 8, p. 76-86

Understanding the risk of burnout, which is a major factor leading to job turnover, is an important means of preventing workers from leaving their jobs. However, for example, the stress check institutionalized by the Ministry of Health, Labor and Welfare is limited to grasping workers' stress situations using questionnaires. Stress is one of the factors that lead to burnout, but it is not possible to accurately grasp the risk of worker burnout just by grasping the situation. In addition, Non-Patent Document 1 discusses the relationship between factors other than stress and burnout, but does not aim to directly determine the risk of burnout in subjects.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a program, a determination system, and a determination device for determining working conditions that can directly determine the risk of worker burnout.

A program for judging a working condition according to the present invention includes stress evaluation obtaining means for obtaining a stress evaluation value indicating the level of stress of a subject, and resilience evaluation obtaining means for obtaining a resilience evaluation value indicating the level of resilience of the subject. , The subject's average sleep time per day is within the range from a predetermined lower limit to the upper limit, and the subject's average bedtime on the day before the day shift is earlier than a predetermined reference time. Evaluation acquiring means, and evaluating whether the stress of the subject is high or low based on a comparison between the stress evaluation value acquired by the stress evaluation acquiring means and a predetermined reference value, and the resilience evaluation acquiring means acquiring Whether the resilience of the subject is high or low is evaluated based on the comparison between the resilience evaluation value and a predetermined reference value, and the stress is low based at least on these evaluation results and the evaluation results obtained by the sleep evaluation obtaining means. , the computer functions as burnout risk determination means for determining that the subject's risk of burnout is low when resilience is high and sleep is good. Further, a program according to another aspect of the present invention includes stress evaluation obtaining means for obtaining a stress evaluation value indicating the level of stress of a subject, and resilience evaluation obtaining means for obtaining a resilience evaluation value indicating the level of resilience of the subject. a sleep evaluation acquisition means for evaluating whether or not the subject sleeps well based on whether or not the subject's average sleep time per day and the subject's average bedtime on the day before the day's shift are within a predetermined range; Evaluating whether the subject's stress is high or low based on a comparison between the stress evaluation value obtained by the stress evaluation obtaining means and a predetermined reference value, and comparing the resilience evaluation value obtained by the resilience evaluation obtaining means with the predetermined reference. The resilience of the subject is evaluated based on comparison with the values, and based at least on these evaluation results and the evaluation results obtained by the sleep evaluation obtaining means, stress is low, resilience is high, and When the sleep is good, the computer functions as burnout risk determination means for determining that the risk of burnout of the subject is low, and the burnout risk determination means determines that even if the stress of the subject is low, If the subject's resilience is low, it is determined that the subject is at high risk of burnout.

Further, the working condition determination device according to the present invention includes stress evaluation acquiring means for acquiring a stress evaluation value indicating the level of stress of the subject, and resilience evaluation acquiring means for acquiring the resilience evaluation value indicating the level of resilience of the subject. Means, the average sleep time per day of the subject is within the range from the predetermined lower limit to the upper limit, and the average bedtime of the subject on the day before the day shift is higher than the predetermined reference time. sleep evaluation obtaining means , and evaluating whether the subject's stress is high or low based on a comparison between the stress evaluation value obtained by the stress evaluation obtaining means and a predetermined reference value, and the resilience evaluation obtaining means obtaining the Whether the resilience of the subject is high or low is evaluated based on the comparison between the resilience evaluation value and a predetermined reference value, and the stress is low based at least on these evaluation results and the evaluation results obtained by the sleep evaluation obtaining means. and burnout risk determination means for determining that the risk of burnout of the subject is low when resilience is high and sleep is good. A determination apparatus according to another aspect of the present invention includes stress evaluation obtaining means for obtaining a stress evaluation value indicating the level of stress of a subject; Acquisition means, and sleep evaluation acquisition means for evaluating whether or not the subject sleeps well based on whether the subject's average sleep time per day and the subject's average bedtime on the day before the day's shift are within a predetermined range. , evaluating whether the subject's stress is high or low based on a comparison between the stress evaluation value obtained by the stress evaluation obtaining means and a predetermined reference value, and comparing the resilience evaluation value obtained by the resilience evaluation obtaining means with a predetermined is evaluated as to whether the subject's resilience is high or low based on the comparison with the reference value, and based at least on these evaluation results and the evaluation results obtained by the sleep evaluation obtaining means, stress is low and resilience is high, and burnout risk determination means for determining that the subject's risk of burnout is low when sleep is good, and the burnout risk determination means determines that the subject's stress is low even if the subject's stress is low. Also, when the subject's resilience is low, the subject's risk of burnout is determined to be high. Furthermore, in the present invention, even if the subject's stress is low and the subject's resilience is high, it is preferable to determine that the risk of burnout is high if the subject is not sleeping well.

According to the working condition determination program or determination device according to the present invention, it is possible to directly determine the burnout risk of a subject. The inventors have also found that a subject's risk of burnout is also strongly associated with the subject's resilience and sleep status. Therefore, the certainty of the determination can be ensured by basing the risk determination not only on the level of stress of the subject but also on the level of resilience and goodness of sleep. The level of stress or resilience and the goodness of sleep are determined, for example, based on comparison between a value indicating the evaluation result and a reference value.

Further, in the present invention, the sleep evaluation means determines that the average sleep time per day of the subject is within a predetermined range from the lower limit to the upper limit and the average bedtime of the subject on the day before the day shift is early. It is preferable to evaluate as good. The present inventors have found that the quality of sleep is low when the average sleeping time is too short or too long, and that the quality of sleep is low when the average bedtime is later. Based on this, the certainty of the determination can be ensured by taking into account the goodness of sleep when determining the risk of burnout.

Further, in the present invention, it is preferable that the burnout risk determination means determines that the risk of burnout of the subject is high when the subject's resilience is low even when the stress of the subject is low. The inventors have found that the risk of burnout is higher when resilience is low, even when stress is low. Based on this, the risk of burnout can be appropriately determined according to the level of resilience.

Further, in the present invention, the burnout risk determination means is based on at least one of the height of coping, the presence or absence of night shift, the age, the presence or absence of raising children, and the presence or absence of a spouse. It is preferable to determine the risk of According to this, it is possible to determine the risk of burnout step by step based on various factors related to the risk of burnout.

Further, the working condition determination system according to the present invention includes the program, the computer, stress item input means for inputting answers to question items for deriving the level of stress of the subject, and the level of resilience of the subject. resilience item input means for inputting answers to question items for deriving stress; and detection means for detecting sleep conditions of the subject; The resilience evaluation acquisition means derives the resilience level of the subject based on the content input by the resilience item input means, and the sleep evaluation acquisition means obtains the A sleep quality of the subject is derived based on the detection result by the detection means. According to this, the level of stress and resilience is derived based on the answers to various question items. Also, the goodness of sleep is derived based on the detection result by the detection means for detecting the sleep condition. The risk of burnout is then determined based on the derived levels of stress and resilience, and the quality of sleep.

1 is a block diagram showing the device configuration of a burnout determination system according to one embodiment of the present invention; FIG. FIG. 2 is a flow diagram showing an algorithm for determination executed in the server of FIG. 1; Figure 3 is a flow diagram illustrating a method of obtaining the algorithm of Figure 2; FIG. 3 is a conceptual diagram showing the transition of implementation results in fiscal 2016-2017 for the algorithm of FIG. 2;

A burnout risk determination system 1 (work condition determination system according to the present invention) according to an embodiment of the present invention will be described below with reference to FIGS. 1 and 2. FIG. The burnout risk determination system 1 is a system that determines the risk of worker burnout (burnout syndrome). The occupations of workers are mainly assumed to be occupations that have night shifts, such as nurses, but any occupation may be the target of this system.

The burnout risk determination system 1 includes an input device 100, an output terminal 200 and a server 300, as shown in FIG. These devices 100-300 are connected to each other through the Internet N. FIG. In the following, it is assumed that when data is transmitted/received between these devices 100 to 300, the transmission/reception is performed through the Internet N. FIG. Each of these devices 100 to 300 includes hardware such as a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), hard disk, input device, and output device, and memory such as ROM and RAM. and software consisting of program data and the like stored in the unit. The functions described below in the burnout risk determination system 1 are realized by the cooperation of these hardware and software.

The input device 100 includes an input terminal 110 and a wearable device 120 (detection means). The input terminal 110 is a terminal for inputting the subject's work situation and the like for determining burnout risk, and may be a mobile terminal such as a smart phone. The input terminal 110 includes an output device such as a display, and an input device such as a keyboard, pointing device, and touch panel. The input terminal 110 causes the display to display input items regarding the subject's work situation. The input person uses the input device of the input terminal 110 to input the contents corresponding to each input item, thereby inputting the labor situation of the subject to the input terminal 110 . The input person may be the subject himself/herself, or may be a person different from the subject. The content of the input to the input terminal 110 is transmitted to the server 300 and used in the determination of burnout risk in the server 300 as described later.

The input items in the input terminal 110 include, for example, the following items related to the subject: items for calculating the stress score indicating the level of stress, items for calculating the coping scale, and for calculating the resilience scale. , child rearing, night shift, marital status, and work schedule (day shift, night shift, vacation days, etc.). The function of the input terminal 110 for inputting items for calculating the stress score corresponds to the function of the stress item input means of the present invention. The function of the input terminal 110 to input items for calculating the resilience scale corresponds to the function of the resilience item input means in the present invention.

For the items for calculating the stress score, for example, the question items of the “Simplified Occupational Stress Questionnaire (Ministry of Health, Labor and Welfare)” are used. Specifically, among the question items of the "Simplified Occupational Stress Questionnaire (Ministry of Health, Labor and Welfare)", 11 items consisting of the following P1 to P11, which are question items for evaluating "physical and mental stress reactions", may be used. These 11 items ask about the subject's condition on that day, with reference to the time of questioning. For each item, the subject is asked to answer the condition of the question item by selecting one from four options: "very applicable", "slightly applicable", "somewhat disagree", and "mostly disagree".

[Items for calculating the stress score]
P1: I am very tired P2: I am exhausted P3: I am tired P4: I am tense P5: I am anxious P6: I am restless P7: I am depressed P8: I am tired of doing anything P9: I am not feeling well P10: I have no appetite No P11: I can't sleep well

As an example of the items for calculating the coping scale, the following question items consisting of Q1 to Q18 are used (Takayuki Kageyama, Toshio Kobayashi: How to deal with "stress" that supports mental health: Coping characteristics by BSCP What can be seen from the evaluation, Kongo Publishing, 2017). For each item, the subject is asked to answer the condition of the question item by selecting one from four options of "often", "sometimes", "occasionally", and "rarely". Coping is a constantly changing cognitive and behavioral effort made for specific external and internal needs rated as burdensome or beyond the individual's resources. (Ibid.). As other question items for calculating the coping scale, 60 items of "Ways of Coping Checklist (WCC)" and 48 items of "Coping Inventory for Stressful Situation (CISS)" may be used.

[Items for calculating the coping scale]
Q1: Investigate the cause and try to solve it Q2: Think about what you have experienced so far Q3: Think calmly about what you can do now Q4: Talk to someone you trust about a solution Q5: Talk with people involved, Try to solve the problem Q6: Ask someone who is knowledgeable about the problem to teach you Q7: Distract yourself with hobbies or entertainment Q8: Do something that calms you down Q9: Do something active, such as traveling or going out, to change your mood Q10: Have hope that "it will work out somehow" Q11: Think about the positive side of the event Q12: Try to think that this is also a good experience for yourself Q13: Blame someone else for the problem Q14: For the problem Blaming the people involved Q15: Taking a hard shot at people unrelated to them Q16: Postponing the problem Q17: Waiting for time to pass thinking that things will change someday Q18: Doing nothing

As an example of the items for calculating the resilience scale, the following question items consisting of R1 to R18 are used (Hiroyuki Ogata: Creation of nurse resilience scale and examination of reliability and validity in general hospitals, Psychiatry , 52(8), pp. 785-792, 2010).
For each item, subjects were asked to choose one from five options: “Yes,” “Somewhat yes,” “Neither,” “Somewhat no,” or “No.” Reply status. Resilience is defined as a state of being well adapted to a difficult and threatening situation, overcoming a temporary state of psychological ill health, not exhibiting mental pathology, and so on. (Mari Hirano: Attempt to classify resilience qualitative factors/acquired factors--Creation of a two-dimensional resilience factor scale (BRS), Personality Research, Vol. 19, No. 2, pp. 94 -106, 2010).

[Items for calculating the resilience scale]
R1: I would like to try various tasks in the nursing profession R2: I am not good at coordinating with bosses and colleagues who do not get along R3: There are people other than the workplace who I love (family, friends, etc.) R4: Unfamiliar work R5: I can keep myself calm even in the event of death or sudden changes R6: I can get along with various types of bosses and colleagues R7: I like new and unusual work R8: Listen to selfishness R9: I manage to get a job even if I have a hard time R10: I tend to have a strong interest in nursing work and patients R11: I think I will do my best if I am entrusted with a big responsibility R12: Nursing R13: I have goals as a nurse R14: I am working hard every day as a nursing professional R15: I want to study more about nursing R16: I hate my boss R17: I can get along well with new bosses/colleagues in the workplace R18: I think that difficult things are necessary for growth as a nursing professional R19: New It is easy to learn a job. R20: There are people other than my family that I can talk to about my worries. R21: I had someone who loved me when I was young. there is

The wearable device 120 is a device that can be worn by the subject, and has a measuring device such as an accelerometer that detects the motion of the subject. The wearable device 120 can communicate with the input terminal 110 by short-range communication such as Bluetooth (registered trademark). A detection result by the wearable device 120 is transmitted to the input terminal 110 . A detection result by the wearable device 120 is transmitted from the input terminal 110 to the server 300 and used in the server 300 to determine burnout risk.

The output terminal 200 outputs the burnout risk determination result transmitted from the server 300 to the user through an output device such as a display or a printing device. Table 1 below shows an example of burnout risk determination results. Table 1 contains 16 levels of judgment values. The lower the judgment value, the higher the risk of burnout. Judgment values 1 to 10 are classified into a "burnout group" in which the risk of burnout is relatively high. Criteria 11, 12, 21, 22 and 31 are classified as a "risk group" with moderate risk of burnout. A judgment value of 100 is classified as a "low group" where the risk of burnout is relatively low. The grounds for the classification of "burnout group", "risk group" and "low group" will be described later. The output result from the output terminal 200 includes, for example, judgment values and their classifications for all or part of the subjects. Moreover, the output result from the output terminal 200 may include statistical values, graphs, and the like regarding the judgment values of a plurality of subjects.

[Table 1]

The server 300 includes a stress score calculation unit 310 (stress evaluation acquisition means in the present invention), a coping scale calculation unit 320, a resilience scale calculation unit 330 (resilience evaluation acquisition means in the present invention), a sleep situation acquisition unit 340, and a risk assessment unit 350. (Burnout risk determination means in the present invention). The program that causes the server 300 to function as these functional units corresponds to the program for judging the working condition in the present invention. The stress score calculator 310 calculates a stress score based on the input results for the question items P1 to P11 transmitted from the input device 100. FIG. The stress score is given by giving 1 point to the question items for which the input result is “very applicable” or “slightly applicable”, and 0 point for the question item for which the input result is “somewhat disagree” or “mostly disagree”. It is calculated for each subject by totaling the scores for all question items P1 to P11.

The coping scale calculator 320 calculates a coping scale based on the input results for the question items Q1 to Q18 transmitted from the input device 100 (however, Q16, Q17 and Q18 are not used). The coping scale is calculated from two viewpoints of problem solving and emotional focus. Hereinafter, the coping scale based on the former viewpoint will be referred to as the "coping scale (problem solving)", and the coping scale based on the latter viewpoint will be referred to as the "coping scale (emotional focus)". The coping scale (problem solving) is calculated for each subject by assigning points to the input results as follows and summing the points for the question items of Q1, Q2, Q3, Q4, Q5 and Q6. be done. For the coping scale (emotional focus), after assigning points to the input results as follows, total the points for Q7, Q8, Q9, Q10, Q11, Q12, Q13, Q14 and Q15. , calculated for each subject.

[Correspondence between input result and score]
“Often” → 4 points, “Sometimes” → 3 points, “Occasionally” → 2 points, “Rarely” → 1 point

The resilience scale calculator 330 calculates a resilience scale based on the input results for the question items R1 to R22 transmitted from the input device 100. FIG. The resilience scale is calculated for each subject by assigning scores to the input results as follows and summing up the scores for all question items R1 to R22.
[Correspondence between input result and score]
(R2, R4, R8) “Yes” → 1 point, “Somewhat yes” → 2 points, “Neither” → 3 points, “Somewhat no” → 4 points, “No” → 5 points (other than R2, R4, R8) “Yes” → 5 points, “Somewhat yes” → 4 points, “Neither” → 3 points, “Somewhat no” → 2 points , "No" → 1 point

Based on the detection result of the wearable device 120 transmitted from the input device 100, the sleep condition acquisition unit 340 derives the sleep condition of the subject as follows. The sleep condition acquisition unit 340 accumulates detection results by the wearable device 120 periodically transmitted from the input device 100 to the server 300 for multiple days. The sleep condition acquisition unit 340 grasps the sleep period of the subject based on the motion of the subject indicated by the detection result of the wearable device 120 . For example, a sleep period is determined to be a period in which the movement of the subject is small by a predetermined amount over a predetermined period of time. Based on the sleep period thus determined, the sleep condition acquisition unit 340 calculates the length of the sleep period per day as the average sleep time, and determines the start time of the sleep period as the bedtime. Also, based on the work schedule input at the input terminal 110, the sleep condition acquisition unit 340 grasps the day of the day shift for the subject. Then, the sleep condition acquisition unit 340 calculates the average bedtime on the day before the day of the grasped day shift.

The risk assessment unit 350 calculates the stress score, the coping scale, the resilience scale, and the sleep conditions derived by the stress score calculation unit 310, the coping scale calculation unit 320, the resilience scale calculation unit 330, and the sleep condition acquisition unit 340, and from the input device 100 A burnout risk determination value is derived as follows based on the transmitted presence/absence of child rearing. Note that the risk evaluation unit 350 includes a sleep evaluation unit 351 (sleep evaluation acquisition means in the present invention). The sleep evaluation unit 351 plays a role of evaluating the goodness of sleep of the subject, as described later, in deriving the burnout risk determination value.

The risk evaluation unit 350 derives the judgment value according to the flowchart of FIG. First, in step S1 of FIG. 2, the height of the stress score is determined. If the stress score is 6 or higher, the risk evaluation unit 350 determines whether or not the child is being raised (step S2). The risk evaluation unit 350 sets the determination value to 1 when there is no child rearing. If there is child rearing, the risk evaluation unit 350 determines whether or not there is a night shift (step S3). If there is a night shift, the risk evaluation unit 350 sets the determination value to 2. If there is no night shift, the risk evaluation unit 350 determines whether or not the stress score is 7 or higher (step S4). If the stress score is 7 or higher, the risk evaluation unit 350 sets the determination value to 6. When the stress score is 6, the risk evaluation unit 350 sets the determination value to 8.

When the stress score is 4 or more and 5 or less in step S1, the risk evaluation unit 350 determines whether or not the age is 30 or more (step S5). If the age is 30 or more, the risk evaluation unit 350 sets the determination value to 3. If the age is less than 30, the risk evaluation unit 350 determines whether the coping scale (emotional focus) is 23 or more (step S6). If the coping scale (emotional focus) is 23 or higher, the risk evaluation unit 350 sets the determination value to 5. If the coping scale (emotional focus) is 22 or less, the risk evaluation unit 350 sets the determination value to 4.

When the stress score is 3 in step S1, the risk evaluation unit 350 determines whether or not the coping scale (problem solving) is 15 or more (step S7). If the coping scale (problem solving) is 15 or higher, the risk evaluation unit 350 sets the determination value to 7. If the coping scale (problem solving) is 14 or less, the risk assessment unit 350 confirms the presence or absence of a spouse (step S8). If there is a spouse, the risk evaluation unit 350 sets the determination value to 9. If there is no spouse, the risk evaluation unit 350 executes the next step S9.

When the stress score is 2 or less in step S1, the risk evaluation unit 350 determines whether or not the resilience scale is 69 or less (step S9). If the resilience scale is 69 or less, the risk evaluation unit 350 sets the determination value to 10. If the resilience scale is 70 or more, the risk evaluation unit 350 executes step S10 and subsequent steps.

Steps after step S10 are steps for deriving a determination value based on the sleep quality of the subject, and are executed by the sleep evaluation unit 351 . The sleep evaluation unit 351 derives a determination value based on the degree of good sleep indicated by the average sleep duration and average bedtime acquired by the sleep condition acquisition unit 340 . Specifically, the sleep evaluation unit 351 first determines the length of the average sleep time (step S10). If the average sleep time is 8 hours or longer, the sleep evaluation unit 351 determines whether the average bedtime on the day before the day shift is 25:30 (1:30 am) or later (step S11). . If the average bedtime is after 25:30, the determination value is 11, and if the average bedtime is before 25:30, the determination value is 12. When the average sleep time is less than 6.8 hours, the sleep evaluation unit 351 determines whether or not the average bedtime on the day before the day shift is after 25:30 (step S12). If the average bedtime is after 25:30, the determination value is 13, and if the average bedtime is before 25:30, the determination value is 14. When the average sleep time is 6.8 hours or more and less than 8 hours, the sleep evaluation unit 351 determines whether or not the average bedtime on the day before the day shift is after 25:30 (step S13). If the average bedtime is after 25:30, the determination value is 15, and if the average bedtime is before 25:30, the determination value is 100. Thus, when the average sleeping time is 6.8 hours or more and less than 8 hours and the average bedtime is up to 25:30, the sleep condition is good and the determination value is 100. Otherwise, it cannot be said that the sleep condition is good, and the determination value is set to each value other than 100 according to the average sleep time and average bedtime.

A method of obtaining the algorithm shown in the flowchart of FIG. 2 will be described with reference to FIG. First, an anonymous/self-administered questionnaire survey on the following survey items was conducted for nurses working in hospitals (excluding the chief nurse) (step S101 in FIG. 3).

[Survey item]
Basic attributes (age, gender, years of service at affiliated hospital, family composition, child rearing status, nursing care status, use of annual leave), work style, stress score, burnout scale, coping scale

In the investigation of the burnout scale, one of the following five options was selected for the following question items (Masato Kubo: Psychology of Burnout: Burnout Syndrome, Science, 2004), and as follows: A score was assigned to each question item.

[Burnout question items]
T1: There are times when I want to quit this kind of work T2: There are times when I am so absorbed in my work that I forget myself T3: There are times when I find it troublesome to pay attention to details T4: This job suits my nature T5: Sometimes I hate seeing the faces of my colleagues and patients T6: Sometimes my work seems boring T8: Before going to work, I sometimes feel that I don't want to go to work and want to stay home T9: After finishing work, I sometimes think that today was a pleasant day T10: Sometimes I don't want to talk about anything with my colleagues or patients T11: Sometimes I don't care about the results of my work T12: Sometimes I feel that my work has left me with no leeway T13: My current job T14: Sometimes I feel that my current job doesn't mean much to me T15: I enjoy my work, but sometimes time passes without me knowing it T16: Both my body and mind T17: I sometimes think that I have finished my work well [response content and score]
"Always" → 5, "Often" → 4, "Sometimes" → 3, "Rarely" → 2, "Never" → 1

Next, as burnout scales, a score indicating emotional exhaustion and a score indicating depersonalization were calculated as follows, and the degree of burnout was evaluated (step S102). A score indicative of emotional exhaustion is obtained by summing the T1, T7, T8, T12 and T16 scores and dividing by 5. The score indicative of depersonalization is obtained by summing the T3, T5, T6, T10, T11 and T14 scores and dividing by 6. Then, when evaluating burnout, each survey subject was divided into a high group, a depersonalized group, an emotional group, and a low group based on whether the score indicating emotional exhaustion and the score indicating depersonalization were above the respective average values. classified into Specifically, if both the score indicating emotional exhaustion and the score indicating depersonalization are above the average value, they are classified into the high group, and if only the score indicating depersonalization is above the average value, they are classified into the depersonalization group. If only the score indicating emotional exhaustion was above the average value, it was classified into the emotional group, and the remaining cases were classified into the low group.

Next, based on the burnout measure evaluated in step S102, items related to burnout were extracted from the survey items (step S103). Specifically, multinomial logistic regression analysis was performed with the burnout measure as the dependent variable. Based on the results, stress score, coping scale, whether or not to raise children, whether or not to work night shifts, and whether or not to have a spouse were extracted from the above survey items as items related to burnout.

Next, based on the items extracted in step S103, an algorithm for classifying subjects into a high group, a depersonalized group, an emotional group, and a low group was acquired (step S104). In acquiring the algorithm, first, using SPSS (registered trademark) v23.0 (IBM Corporation) and SPSS AnswerTree (IBM Corporation), a decision tree consisting of the items extracted in step S103 and branching conditions for each item is generated. Decided. Then, based on the decision tree obtained by this, the first version of the algorithm was created while referring to interviews with nursing managers and researchers on nursing management. The obtained algorithm was almost the same as the decision tree consisting of steps S1 to S8 shown in FIG.

Next, the first version of the algorithm obtained in step S104 was verified (step S105). Specifically, first, the following survey (FY 2016) was conducted using the input device 100 with nurses of one medical facility as subjects. That is, the subjects were asked to input items for calculating the stress score, coping scale, and resilience scale, whether or not they were raising children, whether or not they worked night shifts, whether or not they had a spouse, and their work schedule, into the input terminal 110 . The sleep status was acquired by adding the sleep status as an input item using the input terminal 110 without using the detection result by the wearable device 120 . The same subjects were also surveyed for a burnout scale based on the burnout questionnaire described above. Then, based on the burnout scale survey results using the burnout questionnaire items, the survey subjects were classified into a high group, a depersonalized group, an emotional group, and a low group, similar to the burnout evaluation in step S102. On the other hand, based on the stress score, the coping scale, the presence or absence of raising children, the presence or absence of night shifts, and the presence or absence of a spouse, the first edition algorithm obtained in step S104 is applied to the high group, the depersonalized group, the emotional Classification of subjects into group and low group was performed. Based on the results of the burnout scale survey using the above-mentioned burnout question items, we evaluated whether the classification results obtained by the algorithm's judgment indicated correct content. As a result, the ratio of the number of subjects for whom the classification result by the algorithm judgment showed correct content to the total number of subjects was 76.7%.

Next, in order to improve the accuracy of the algorithm, the algorithm shown in FIG. 2 was created with additional items relating to the resilience scale and sleep (step S106). The items added here correspond to steps S9 to S13 in FIG. The resilience scale and sleep items are added because the results of the investigation conducted in step S104 show that the resilience scale and sleep conditions are also strongly related to burnout. Branching conditions for resilience measures and sleep conditions in the algorithm were derived based on actual measurements of resilience measures and sleep time obtained from the survey results.

Next, the algorithm created in step S106 was verified based on the investigation results executed in step S105 (step S107). Specifically, the results determined based on the algorithm in FIG. 2 were evaluated in the same manner as step S105 in FIG. ). The evaluation results are shown in Table 2.

[Table 2]

In Table 2, the leftmost column shows the classification of burnout group, risk group and low group as in Table 1. The second column from the left shows determination values derived by the server 300 . The third column from the left shows the number of subjects corresponding to the judgment value. The fourth column from the left shows the number of people whose classification result corresponding to the judgment value was correct. The fifth column from the left shows the number of people whose classification result corresponding to the judgment value was incorrect. The sixth column from the left shows the discrimination detection rate, which corresponds to the ratio of the number of people in the fourth column to the number of people in the third column. The seventh column from the left shows the classification error rate, corresponding to the ratio of the number of people in the fifth column to the number of people in the third column.

In Table 2, the "burnout group" corresponds to the combined group of high, depersonalized and affective on the burnout scale. The "risk group" was classified as a low group on the burnout scale in the first version of the algorithm, but it is not a low group and has a lower risk of burnout than the burnout group, but will be shifted to the burnout group in the future. classified as at-risk group. "Low group" corresponds to the low group on the burnout scale. This group belongs to the range that was classified as a low group in the burnout scale even in the first version of the algorithm, and is a group with a low possibility of becoming a burnout state at present.

In Table 2, whether or not the classification result is correct when the judgment value is 1 to 10 (fourth and fifth columns from the left) is determined by the burnout scale survey based on the burnout questionnaire. It is based on whether or not the content was certainly classified into the high group, the depersonalized group, and the emotional group. In addition, whether or not the classification result is correct when the judgment value is 100 (fourth and fifth columns from the left) is a survey of the burnout scale based on the burnout questionnaire. based on whether the content was classified as Regarding whether the classification results were correct when the judgment value was 11 to 15 (columns 4 and 5 from the left), the burnout scale was certainly low in the survey of the burnout scale based on the burnout questionnaire. Based on whether or not the content was classified outside the group. Note that the range in which the judgment value is 11 to 15 is a range classified as a low group in the first version of the algorithm in step S104, but as described above, a risk group with a risk of transitioning to the burnout group. This is the range reclassified as Therefore, in this range, the classification result (being in the low group) based on the burnout scale is not necessarily correctly judged.

Next, regarding some of the subjects in the survey (FY 2016) conducted in step S105, the determination and survey using the same algorithm in FIG. 2 were performed in the following year as well. FIG. 4 shows changes in the determination results of the same subjects from fiscal 2016 to 2017, and also shows the subjects' retirement status and retirement intention (retirement hope or retirement intention). In Figure 4, the numbers aligned vertically under the column name of "FY 2016" indicate the judgment results for FY 2016, and the numbers aligned vertically under the column name of "FY 2017" indicate the judgment results for FY 2017. show. An arrow pointing from the numerical value in 2016 to the numerical value in 2017 indicates how the subject corresponding to each judgment value in 2016 was judged in 2017. For example, an arrow pointing from a judgment value of 4 in 2016 to a judgment value of 1 in 2017 indicates that a subject who had a judgment value of 4 in 2016 had a judgment value of 1 in 2017. For each arrow, the thicker the line, the larger the number of subjects corresponding to the transition of the judgment value indicated by the arrow. In addition, the arrows from the judgment value in 2016 or the judgment value in 2017 to "retirement", "retirement hope", and "retirement hesitation" indicate whether the subject corresponding to each judgment value in 2016 or 2017 actually retired, It indicates that the employee wishes to retire or is unsure about retiring. As shown in Figure 4, most of the retirees and retirees come from the burnout group in the most recent determination and some from the risk group. Retirees and retirees do not come from the low group in the most recent determination. Many of the subjects who were judged to have a judgment value of 1 to 10 (burnout group) in 2016 were also judged to have a judgment value of 1 to 10 (burnout group) in 2017. The reasons for classifying judgment value 10 into the burnout group instead of the risk group are, as described above, that there are many retirees and those who intend to retire in judgment values 1 to 10, and judgment values 1 to 10 in 2016 It is based on the fact that the judged subjects have a strong tendency to be judged in the same way in 2017.

According to the present embodiment described above, it is possible to directly determine the burnout risk of a subject. In particular, as shown in FIG. 2, the stress score is a low level of 2 or less (step S1: "2 or less"), the resilience scale is a high level of 70 or more (step S9: "70 or more"), and If the sleep is good (step S13: "Until 25:30"), the judgment value is 100, that is, the risk is judged to be low. In this way, the algorithm in FIG. 2 is based not only on the level of stress of the subject in risk determination, but also on the level of resilience and goodness of sleep, so that the discrimination detection rate shown in Table 2 above is used. certainty is guaranteed.

Also, according to the algorithm of FIG. 2, when the average sleeping time is 6.8 hours or more and less than 8 hours, and the average bedtime is before 25:30, the risk is determined to be low. That is, if the average sleeping time is too short or too long, the quality of sleep is low, and if the average bedtime is later, the quality of sleep is low. Based on the sleep quality evaluated in this way, the certainty of determination is ensured.

Further, as shown in the algorithm of FIG. 2, even if the stress is low (stress score is 2 or less), if the resilience is low (resilience scale is 69 or less), the judgment value becomes 10 (step S9: " 69 or less"). In other words, the subject is determined to be in the burnout group. In this way, the risk of burnout is appropriately determined according to the level of resilience.

In addition, as shown in the algorithm of FIG. 2, the subject's risk of burnout is determined stepwise based on coping height, night shift work, age, child rearing status, and marital status. According to this, it is possible to determine the risk of burnout step by step based on various factors related to the risk of burnout.

<Modification>
Although the preferred embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and various modifications can be made within the scope described in the Means for Solving the Problems. It is possible.

For example, in the algorithm of FIG. 2 according to the embodiment described above, the subject's sleep condition is evaluated based on the average sleep time and the average bedtime on the day before the day shift. However, sleep status may be evaluated based on other items. For example, the sleep status may be evaluated based on the standard deviation of sleep time, average wake-up time, or the like. Moreover, in the above-described embodiment, the sleep status is evaluated based on whether the average sleep time is 6.8 hours or more and less than 8 hours. However, evaluation criteria other than 6.8 hours or 8 hours may be used depending on the work environment, occupation, and the like. For example, 6 hours, 7.5 hours, etc. may be used.

Also, in the above-described embodiments, the wearable device 120 is used to detect the sleep condition of the subject. However, the subject can directly input the daily bedtime, wake-up time, day shift/night shift into the input device 100, and based on the input content, the server 300, etc. The system 1 may be configured to derive bedtime.

Also, in the above-described embodiment, the sleep condition acquisition unit 340 of the server 300 grasps the sleep period of the subject based on the detection result of the wearable device 120 . However, the input terminal 110 may grasp the sleep time based on the detection result in the wearable device 120 . In this case, the input terminal 110 may transmit the sleep time and the wake-up time to the server 300 based on the sleep period that has been grasped. Then, the server 300 calculates the average sleep time and the average bedtime of the day before the day shift from the bedtime and wake-up time transmitted from the input terminal 110 . Note that the input terminal 110 may calculate the average sleep time and the average bedtime of the day before the day shift, and transmit the calculation result to the server 300 .

Further, in the above-described embodiment, the stress score calculation unit 310 and the resilience scale calculation unit 330 as the stress evaluation obtaining means and the resilience evaluation obtaining means obtain the stress score and the resilience scale based on the information transmitted from the input device 100, That is, the evaluation result of the level of stress and the level of resilience are calculated. However, the server 300 may be provided with means of another mode as a means for acquiring the evaluation results of the level of stress and the level of resilience. For example, the server 300 may be provided with means for receiving stress scores and resilience measures calculated or entered in other devices over a communication network. The server 300 may also be provided with means for directly inputting stress scores and resilience measures.

Further, in the above-described embodiment, the sleep evaluation unit 351, as the sleep evaluation obtaining means, calculates the average sleep time and the average bedtime on the day before the day shift, and evaluates the goodness of sleep. However, the server 300 may be provided with means of another aspect as means for acquiring evaluation results of sleep conditions. For example, the server 300 may be provided with means for receiving, through a communication network, the average hours of sleep and the average bedtime of the day before the working day calculated or input in another device. Further, the server 300 may be provided with means for directly inputting the average sleeping hours and the average bedtime of the day before the day shift.

Also, the algorithm of FIG. 2 according to the above-described embodiment is created based on a survey of nurses. However, algorithms designed to survey workers in other occupations may also be used. Even in this case, using a decision tree that includes the three items of stress level, resilience level, and sleep quality improves the certainty of determining the risk of burnout. In addition, items different from those in the above-described embodiment may be used as question items for evaluating the level of stress and resilience. For example, "CFSI: cumulative fatigue symptom index (Koshikawa, 1990)" may be used as a question item for evaluating the level of stress. In addition, a “two-dimensional resilience scale (Hirano, 2010)” may be used as a question item for evaluating the level of resilience.

1 burnout risk determination system 100 input device 110 input terminal 120 wearable device 200 output terminal 300 server 310 stress score calculation unit 320 coping scale calculation unit 330 resilience scale calculation unit 340 sleep condition acquisition unit 350 risk evaluation unit 351 sleep evaluation unit

Claims (7)

stress evaluation acquiring means for acquiring a stress evaluation value indicating the level of stress of the subject;
resilience evaluation acquisition means for acquiring a resilience evaluation value indicating the level of resilience of the subject;
Sleep evaluation that evaluates that sleep is good when the subject's average sleep time per day is within a range from a predetermined lower limit to an upper limit, and when the subject's average bedtime on the day before the day shift is earlier than a predetermined reference time Acquisition means, and
Evaluate whether the stress of the subject is high or low based on a comparison between the stress evaluation value obtained by the stress evaluation obtaining means and a predetermined reference value, and compare the resilience evaluation value obtained by the resilience evaluation obtaining means with a predetermined reference value . Evaluating whether the resilience of the subject is high or low based on comparison with the reference value, and based at least on these evaluation results and the evaluation results obtained by the sleep evaluation obtaining means, stress is low, resilience is high, and 1. A program for judging working conditions characterized by causing a computer to function as burnout risk judging means for judging that the risk of burnout of a subject is low when sleep is good. stress evaluation acquiring means for acquiring a stress evaluation value indicating the level of stress of the subject;
resilience evaluation acquisition means for acquiring a resilience evaluation value indicating the level of resilience of the subject;
sleep evaluation acquisition means for evaluating whether the subject sleeps well or not based on whether the subject's average sleep time per day and the subject's average bedtime on the day before the day's shift are within a predetermined range; and
Evaluate whether the stress of the subject is high or low based on a comparison between the stress evaluation value obtained by the stress evaluation obtaining means and a predetermined reference value, and compare the resilience evaluation value obtained by the resilience evaluation obtaining means with a predetermined reference value . Evaluating whether the resilience of the subject is high or low based on comparison with the reference value, and based at least on these evaluation results and the evaluation results obtained by the sleep evaluation obtaining means, stress is low, resilience is high, and , When the sleep is good, the computer functions as a burnout risk determination means that determines that the risk of burnout of the subject is low ,
The burnout risk determination means is
A program for judging working conditions characterized by judging that a subject has a high risk of burnout if the subject's resilience is low even if the stress of the subject is low. Even if the subject's stress is low and the subject's resilience is high, if the subject's sleep is not good, it is determined that the risk of burnout is high. judgment program. The burnout risk determination means is
Claims characterized in that the subject's burnout risk is determined step by step based on at least one of coping height, night shift work, age, child rearing status, and marital status. A program for judging the working condition according to 2 or 3. A program according to any one of claims 1 to 4;
the computer;
stress item input means for inputting answers to question items for deriving the level of stress of the subject;
resilience item input means for inputting answers to question items for deriving the level of resilience of the subject;
a detection means for detecting the sleep status of the subject,
the stress evaluation obtaining means derives the level of stress of the subject based on the content input by the stress item input means;
The resilience evaluation acquiring means derives the level of resilience of the subject based on the content input by the resilience item input means,
The working condition determination system, wherein the sleep evaluation obtaining means derives the sleep quality of the subject based on the detection result of the detection means. stress evaluation acquiring means for acquiring a stress evaluation value indicating the level of stress of the subject;
resilience evaluation acquisition means for acquiring a resilience evaluation value indicating the resilience level of the subject;
Sleep evaluation that evaluates that sleep is good when the subject's average sleep time per day is within a range from a predetermined lower limit to an upper limit, and when the subject's average bedtime on the day before the day shift is earlier than a predetermined reference time acquisition means;
Evaluate whether the stress of the subject is high or low based on a comparison between the stress evaluation value obtained by the stress evaluation obtaining means and a predetermined reference value, and compare the resilience evaluation value obtained by the resilience evaluation obtaining means with a predetermined reference value . Evaluating whether the resilience of the subject is high or low based on comparison with the reference value, and based at least on these evaluation results and the evaluation results obtained by the sleep evaluation obtaining means, stress is low, resilience is high, and and burnout risk determination means for determining that the subject's risk of burnout is low when sleep is good. stress evaluation acquiring means for acquiring a stress evaluation value indicating the level of stress of the subject;
resilience evaluation acquisition means for acquiring a resilience evaluation value indicating the resilience level of the subject;
sleep evaluation acquisition means for evaluating whether the subject sleeps well or not based on whether the subject's average sleep time per day and the subject's average bedtime on the day before the day's shift are within a predetermined range;
Evaluate whether the stress of the subject is high or low based on a comparison between the stress evaluation value obtained by the stress evaluation obtaining means and a predetermined reference value, and compare the resilience evaluation value obtained by the resilience evaluation obtaining means with a predetermined reference value . Evaluating whether the resilience of the subject is high or low based on comparison with the reference value, and based at least on these evaluation results and the evaluation results obtained by the sleep evaluation obtaining means, stress is low, resilience is high, and , and burnout risk determination means for determining that the risk of burnout of the subject is low when the sleep is good ,
The burnout risk determination means is
1. A working condition determination device that determines that a subject has a high risk of burnout if the subject's resilience is low even when the subject's stress is low.

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