JP2020086818A - Program for determining labor status, determination system, and determination device - Google Patents

Abstract

To enable directly determining risks in burnout for workers.SOLUTION: In an input terminal 110, it causes to input responses against questioning items for calculating a stress score and a resilience scale for a subject. A wearable device 120 obtains a sleep state of the subject. A server 300 calculates the stress score and the resilience scale based on the aforementioned responses and evaluates a satisfactory level of sleep. Then, when the stress is low, the resilience is high, and the sleep is satisfactory, based on the stress score, the resilience scale and the satisfactory level of the sleep state, the server 300 determines that the subject has low risks in burnout.SELECTED DRAWING: Figure 1

Description

The present invention relates to a program for judging a working state, a judgment system, and a judgment device.

Conventionally, there has been a problem of leaving the work of workers such as nursing staff. Burnout (burnout syndrome) is one of the causes of job separation, and various research reports have been made. Non-Patent Document 1 is an example of the study, and is an examination of 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 Occupational Safety and Health Act, mainly for the purpose of preventing mental health problems of 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 significant factor leading to employee turnover, is an important measure to prevent workers from leaving their jobs. However, for example, the stress check systemized by the Ministry of Health, Labor and Welfare is limited to grasping the stress situation of workers using questionnaires. Although stress is one of the factors leading to burnout, it is not possible to accurately understand the risk of burnout for workers by only understanding the situation. Although Non-Patent Document 1 discusses the relationship with burnout with respect to factors other than stress, it is not intended to directly judge the risk of burnout in a subject.

An object of the present invention is to provide a program, a determination system, and a determination device for determining a working condition that can directly determine the risk of burnout of a worker.

The program for determining the working state according to the present invention is a stress evaluation acquisition unit that acquires an evaluation result regarding the height of stress of a subject, a resilience evaluation acquisition unit that acquires an evaluation result regarding the height of resilience of the subject, and sleep of the subject. Based on at least the sleep evaluation acquisition means for acquiring the evaluation result regarding the goodness of the evaluation result, and the stress evaluation acquisition means, the resilience evaluation acquisition means and the sleep evaluation acquisition means, the stress is low and the resilience is high. Further, when the sleep is good, the computer is caused to function as a burnout risk determination means for determining that the risk of burnout of the subject is low.

Further, the work state determination device according to the present invention is a stress evaluation acquisition unit that acquires an evaluation result regarding the height of stress of the subject, a resilience evaluation acquisition unit that acquires an evaluation result regarding the height of the resilience of the subject, and the subject. Based on at least the evaluation results obtained by the sleep evaluation acquisition means for acquiring the evaluation result regarding the sleep goodness degree, the stress evaluation acquisition means, the resilience evaluation acquisition means and the sleep evaluation acquisition means, the stress is low and the resilience is The burnout risk determining means determines that the risk of burnout of the subject is low when the sleep rate is high and the sleep is good.

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

Further, in the present invention, the sleep evaluation means, if the average sleep time per day of the subject is within a range from a predetermined lower limit to the upper limit, and if the average bedtime of the subject's day before work is early, sleep is early. It is preferable to evaluate as good. The present inventors have found that the sleep goodness is low when the average sleep time is too short or too long, and the sleep goodness is low as the average bedtime is late. Based on this, the certainty of the judgment can be secured by taking the goodness of sleep into consideration in the risk judgment of burnout.

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

Further, in the present invention, the burnout risk determination means, the burnout of the subject based on at least one of the height of coping, the presence or absence of night shift, the height of age, the presence or absence of child rearing and the presence or absence of a spouse. It is preferable that the risk of is determined stepwise. According to this, it is possible to judge the burnout risk stepwise based on various factors related to the burnout risk.

Further, the work status determination system according to the present invention includes the program, the computer, a stress item input means for inputting an answer to a question item for deriving the stress level of the subject, and a high resilience level of the subject. The resilience item input means for inputting the answer to the question item for deriving the height, and the detection means for detecting the sleep condition of the subject are provided, and the stress evaluation acquisition means inputs by the stress item input means. Derives the stress level of the subject based on the content, the resilience evaluation acquisition means, derives the height of the subject's resilience based on the input content by the resilience item input means, the sleep evaluation acquisition means, The goodness of sleep of the subject is derived based on the detection result by the detection means. According to this, the height of stress and resilience is derived based on the answers to various question items. In addition, the goodness of sleep is derived based on the detection result by the detection unit that detects the sleep situation. Then, the risk of burnout is determined based on the derived high levels of stress and resilience and the goodness of sleep.

It is a block diagram showing a device configuration of a burnout determination system according to an embodiment of the present invention. It is a flowchart which shows the algorithm for the determination performed in the server of FIG. 3 is a flow diagram illustrating a method of obtaining the algorithm of FIG. It is a conceptual diagram which shows the transition of the implementation result of 2016-2017 about the algorithm of FIG.

A burnout risk determination system 1 (working state 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. The burnout risk determination system 1 is a system that determines the risk of burnout (burnout) of workers. The job types of workers are mainly assumed to be jobs such as nurses that have night shifts, but any job type may be the target of this system.

As shown in FIG. 1, the burnout risk determination system 1 includes an input device 100, an output terminal 200, and a server 300. These devices 100 to 300 are connected to each other via the Internet N. In the following, when data is transmitted/received between these devices 100 to 300, the transmission/reception is performed through the Internet N. Each of these devices 100 to 300 has hardware such as a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), a hard disk, an input device and an output device, and a storage such as a ROM and a RAM. And software including 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 working status of the subject for determining the burnout risk, and a mobile terminal such as a smartphone may be used. The input terminal 110 includes output devices such as a display, and input devices such as a keyboard, a pointing device, and a touch panel. The input terminal 110 causes the display to display input items relating to the working condition of the subject. The input person inputs the work status of the subject to the input terminal 110 by inputting the contents corresponding to each input item using the input device of the input terminal 110. The input person may be the subject himself or a person different from the subject. The contents input to the input terminal 110 are transmitted to the server 300, and are used by the server 300 to determine the burnout risk, as described later.

The input items on the input terminal 110 include, for example, the following items related to a subject: an item for calculating a stress score indicating a stress level, an item for calculating a coping scale, and a resilience scale. Items, whether or not to raise children, whether or not to work at night, whether or not there is a spouse, and work schedule (day shift, night shift, vacation days, etc.). The function of the input terminal 110 for inputting the item for calculating the stress score corresponds to the function of the stress item input means in the present invention. The function of allowing the input terminal 110 to input an item for calculating the resilience scale corresponds to the function of the resilience item input means in the present invention.

As an example of the item for calculating the stress score, a question item of the “Simple occupational stress questionnaire (Ministry of Health, Labor and Welfare)” is used. Specifically, among the question items of the “Short-term occupational stress questionnaire (Ministry of Health, Labor and Welfare)”, 11 items including P1 to P11 below, which are question items for evaluating “stress response of mind and body”, may be used. These eleven items ask about the condition of the subject on the day, based on the time of question. For each item, the subject's state regarding the question item is answered by a method of selecting one from four options of “very applicable”, “a little applicable”, “a little different”, and “almost different”.

[Items for calculating stress score]
P1: I'm so tired P2: I'm tired P3: I'm tired P4: I'm enthusiastic P5: I'm anxious P6: I'm resting P7: I'm so depressed P8: I'm tired of doing P9: I'm not feeling comfortable P10: My appetite No P11: I can't sleep well

As an example of the items for calculating the coping scale, the following question items 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's state regarding the question item is answered by a method of selecting one from four options of “frequent”, “sometimes present”, “occasional” and “nearly present”. It should be noted that coping is a constantly changing cognitive-behavioral effort that is made for specific external or internal demands that are assessed as burdensome or exceeding individual resources. (Ibid.). As other question items for calculating the coping scale, 60 items of "Ways of Coping Checklist (WCC)" and 48 items of "Coping Investor for Stressful Status (CISS)" may be used.

[Items for calculating the coping scale]
Q1: Try to find out the cause and try to solve it Q2: Think based on your experience Q3: Think calmly about what you can do now Q4: Consult a reliable person about the solution Q5: Discuss with related people, Solving problems Q6: Asking someone who is familiar with the problem to teach you Q7: Distracting yourself with hobbies and entertainment Q8: Trying to calm yourself down Q9: Traveling, going out, and changing your mood Q10: I hope I can do something Q11: Think of the good side of the event Q12: Try to think that this is also a good experience for me Q13: I blame someone for the cause of the problem Q14: For the problem Blame the people involved Q15: Hit the unrelated people eight Q16: Postpone the problem Q17: Wait for the time to pass, thinking that things will change someday Q18: Do nothing

As an example of the item for calculating the resilience scale, the question items consisting of the following R1 to R18 are used (Hiroyuki Ogata: Creation of nurse resilience scale in general hospital and examination of reliability/validity, psychiatry) , 52(8), pp. 785-792, 2010).
For each item, by selecting one from the five options of "yes", "somewhat yes", "neither", "somewhat no", and "no" Ask the state to answer. In addition, resilience refers to a state in which even if a person is temporarily put into a psychologically unhealthy state due to being exposed to a difficult and threatening state, he/she overcomes it, shows no psychological pathology, and is well adapted. It is supposed to be a concept to be pointed out (Mari Hirano: Attempt to classify qualitative factors and acquisition factors of resilience --- Creation of two-dimensional resilience factor scale (BRS), Personality Study, Vol. 19, No. 2, pp.94. -106, 2010).

[Items for calculating resilience scale]
R1: I would like to try various tasks of nursing staff R2: I am not good at matching with unfamiliar bosses and colleagues R3: There are objects (family, friends, etc.) to pour love other than the workplace R4: Unfamiliar work I don't like to do R5: I can calm myself even in the case of death or sudden changes R6: I can keep up with various types of bosses and colleagues R7: I like new work and unusual work R8: Listen to selfishness There is no one to receive it R9: I can manage to work even if I have a hard time R10: I have a strong interest in nursing work and patients R11: I think I will do my best if I have a great responsibility R12: Nursing staff I have hopes for my future R13: I have a goal as a nursing staff R14: I am working hard every day as a nursing professional R15: I would like to study nursing more R16: My disliked boss ・Rather than being able to work together with my colleagues as a work R17: I can do well even if a new boss or colleague enters the workplace R18: I think that difficult things are necessary for growth as a nursing professional R19: New It's easy to learn work R20: There are people other than family members who can talk about troubles R21: There were people who gave their affection to themselves when they were young R22: People who say "I'm thanks to this person" There is

The wearable device 120 is a device that can be worn by the subject, and has a measuring instrument such as an accelerometer that detects the movement of the subject. The wearable device 120 can communicate with the input terminal 110 by short-range communication such as Bluetooth (registered trademark). The detection result by the wearable device 120 is transmitted to the input terminal 110. The detection result by the wearable device 120 is transmitted from the input terminal 110 to the server 300, and is used by the server 300 to determine the 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 the determination result of the burnout risk. Table 1 contains 16-step judgment values. The smaller the judgment value, the higher the risk of burnout. The judgment values 1 to 10 are classified into a "burnout group" having a relatively high risk of burnout. The judgment values 11, 12, 21, 22, and 31 are classified into a "risk group" in which the risk of burnout is medium. The judgment value 100 is classified into the "low group" which has a relatively low risk of burnout. The basis for classification of “burnout group”, “risk group”, and “low group” will be described later. The output result by the output terminal 200 includes, for example, the judgment value and the classification of all or some of the subjects. In addition, the output result from the output terminal 200 may include statistical values, graphs, and the like regarding the determination values of a plurality of test subjects.

[Table 1]

The server 300 includes a stress score calculation unit 310 (stress evaluation acquisition unit in the present invention), a coping scale calculation unit 320, a resilience scale calculation unit 330 (resilience evaluation acquisition unit in the present invention), a sleep condition acquisition unit 340, and a risk evaluation 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 determining the working status in the present invention. The stress score calculation unit 310 calculates a stress score based on the input result for the question items P1 to P11 transmitted from the input device 100. The stress score is given 1 point for question items whose input results are “very applicable” or “a little applicable”, and 0 points for question items whose input results are “a little different” or “almost different”, It is calculated for each subject by adding up the scores for all question items P1 to P11.

The coping scale calculation unit 320 calculates the 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, a problem solving viewpoint and an emotional focus viewpoint. Hereinafter, the coping scale from the former viewpoint will be referred to as “coping scale (problem solving)”, and the coping scale from the latter viewpoint will be referred to as “coping scale (emotional focus)”. The coping scale (problem solving) is calculated for each subject by assigning scores to the input results as shown below and then adding up the scores for Q1, Q2, Q3, Q4, Q5 and Q6 question items. To be done. For the coping scale (emotional focus), add scores to the input results as shown below, and then add the scores to Q7, Q8, Q9, Q10, Q11, Q12, Q13, Q14, and Q15 questions. Is calculated for each subject.

[Correspondence between input results and points]
“Frequent”→4 points, “Sometimes”→3 points, “Sometimes”→2 points, “Almost no”→1 point

The resilience scale calculation unit 330 calculates the resilience scale based on the input results for the question items R1 to R22 transmitted from the input device 100. The resilience scale is calculated for each subject by assigning scores to the input results as follows and then summing the scores for all question items R1 to R22.
[Correspondence between input results and points]
(For R2, R4, R8) "Yes" → 1 point, "somewhat yes" → 2 points, "neither" → 3 points, "somewhat no" → 4 points, "No" → 5 points (except for R2, R4, R8) "Yes" → 5 points, "somewhat yes" → 4 points, "neither" → 3 points, "somewhat no" → 2 points , "No" → 1 point

The sleep condition acquisition unit 340 derives the sleep condition of the subject as follows based on the detection result of the wearable device 120 transmitted from the input device 100. The sleep condition acquisition unit 340 accumulates the detection results of the wearable device 120, which are periodically transmitted from the input device 100 to the server 300, for a plurality of days. The sleep status acquisition unit 340 grasps the sleep period of the subject based on the movement of the subject indicated by the detection result of the wearable device 120. For example, a period in which the subject's movement is small to a predetermined extent over a predetermined time is determined as a sleep period. Then, from the sleep period thus grasped, the sleep condition acquisition unit 340 calculates the length of the sleep period per day as the average sleep time and grasps the start time of the sleep period as the bedtime. In addition, the sleep status acquisition unit 340 grasps the day on which the subject has a day shift based on the work schedule input at the input terminal 110. Then, the sleep condition acquisition unit 340 calculates an average of bedtime on the day before the day when the grasped day shift is performed.

The risk evaluation unit 350 uses the stress score calculation unit 310, the coping scale calculation unit 320, the resilience scale calculation unit 330, and the sleep status acquisition unit 340 to derive the stress score, the coping scale, the resilience scale and the sleep status, and the input device 100. The judgment value of the burnout risk is derived as follows based on the transmitted child rearing or the like. 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 in evaluating the sleep quality of the subject, as described below, when deriving the determination value of the burnout risk.

The risk evaluation unit 350 derives a judgment value according to the flowchart of FIG. First, in step S1 of FIG. 2, the height of the stress score is determined. When the stress score is 6 or more, the risk evaluation unit 350 determines whether or not the child has been raised (step S2). When the child is not raised, the risk evaluation unit 350 sets the determination value to 1. When the child is raised, the risk evaluation unit 350 determines whether or not there is a night shift (step S3). When there is a night shift, the risk evaluation unit 350 sets the determination value to 2. When there is no night shift, the risk evaluation unit 350 determines whether the stress score is 7 or more (step S4). When the stress score is 7 or more, 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). When the age is 30 or more, the risk evaluation unit 350 sets the determination value to 3. When the age is less than 30, the risk evaluation unit 350 determines whether the coping scale (emotional focus) is 23 or more (step S6). When the coping scale (emotional focus) is 23 or more, the risk evaluation unit 350 sets the determination value to 5. When 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 the coping scale (problem solving) is 15 or more (step S7). When the coping scale (problem solving) is 15 or more, the risk evaluation unit 350 sets the determination value to 7. When the coping scale (problem solving) is 14 or less, the risk evaluation unit 350 confirms the presence or absence of a spouse (step S8). When there is a spouse, the risk evaluation unit 350 sets the determination value to 9. If there is no spouse, the risk evaluation section 350 executes the next step S9.

When the stress score is 2 or less in step S1, the risk evaluation unit 350 determines whether the resilience scale is 69 or less (step S9). When the resilience scale is 69 or less, the risk evaluation unit 350 sets the determination value to 10. When the resilience scale is 70 or more, the risk evaluation unit 350 executes step S10 and subsequent steps.

The steps after step S10 are steps for deriving a determination value based on the degree of sleep of the subject, and are executed by the sleep evaluation unit 351. The sleep evaluation unit 351 derives a determination value based on the average sleep time acquired by the sleep state acquisition unit 340 and the goodness of sleep indicated by the average bedtime. Specifically, the sleep evaluation unit 351 first determines the length of the average sleep time (step S10). When the average sleep time is 8 hours or more, the sleep evaluation unit 351 determines whether or not the average bedtime on the day before the day shift is 25:30 (1:30 am) or later (step S11). .. The judgment value is 11 when the average bedtime is after 25:30, and 12 when the average bedtime is up to 25:30. When the average sleep time is less than 6.8 hours, the sleep evaluation unit 351 determines whether the average bedtime on the day before the day shift is 25:30 or later (step S12). The judgment value is 13 when the average bedtime is after 25:30, and 14 when the average bedtime is until 25:30. When the average sleep time is 6.8 hours or more and less than 8 hours, the sleep evaluation unit 351 determines whether the average bedtime on the day before the day shift is 25:30 or later (step S13). The judgment value is 15 when the average bedtime is after 25:30, and is 100 when the average bedtime is before 25:30. In this way, when the average sleep time is 6.8 hours or more and less than 8 hours and the average bedtime is up to 25:30, the sleeping situation is good and the determination value is 100. In other cases, the sleep condition is not good, and the determination value is determined to be a value other than 100 according to the average sleeping time and the average bedtime.

A method of acquiring the algorithm shown in the flowchart of FIG. 2 will be described based on FIG. First, an anonymous and self-administered questionnaire survey on the following survey items was conducted for nurses (excluding the chief nurse) who work at a hospital (step S101 in FIG. 3).

[Survey item]
Basic attributes (age, gender, number of years of work at the hospital to which you belong, family composition, child rearing, nursing care, annual leave), work style, stress score, burnout scale, coping scale

In the survey of the burnout scale, the following question items (Masato Kubo: Psychology of burnout: What is burnout is Science, 2004) are asked to select one from the following five options, and A score was given to each question item.

[Burnout Question Item]
T1: I want to quit this kind of work anymore T2: I'm so enthusiastic about my work that I forget about me T3: I sometimes find it painful to take care of my pieces T4: This work suits my gender T5: Sometimes it makes me uncomfortable to see the faces of my colleagues and patients T6: My work seems boring, and there is nothing I can do. T7: After the day's work I feel like I'm done.” T8: I don't like going to work before going to work, and I want to stay at home. T9: After work, I think it was a pleasant day. T10: Sometimes I don't want to talk to my colleagues or patients T11: I don't care about the results of my work T12: I sometimes feel that I'm less relaxed because of work T13: My current job , I feel joy from the bottom of my heart T14: I don't think my current work makes much sense to me T15: My work is so fun that I may have too much time before I know it T16: My body and feelings I may be tired. T17: I may think that I was able to finish my work well while being told [contents of answers and points].
“Always”→5, “Often”→4, “Sometimes”→3, “Rarely”→2, “Not”→1

Next, as a burnout scale, a score indicating emotional exhaustion and a score indicating depersonalization were calculated as follows, and the degree of burnout was evaluated (step S102). The score indicating emotional exhaustion is the sum of the scores of T1, T7, T8, T12 and T16, and divided by 5. The score indicating depersonalization is obtained by adding the scores of T3, T5, T6, T10, T11, and T14 and dividing by 6. In the evaluation of burnout, the subjects of each survey were classified into the high group, the depersonalized group, the emotional group and the low group based on whether the score indicating emotional exhaustion and the score indicating depersonalization were equal to or higher than their respective average values. Classified into. Specifically, if both the score indicating emotional exhaustion and the score indicating depersonalization are above the average value, then it is in the high group, and if the only score indicating depersonalization is above the average value, it is in the depersonalization group. , When the score showing emotional exhaustion was above the average, it was classified into emotional group, and the rest was classified into low group.

Next, based on the burnout scale evaluated in step S102, items related to burnout were extracted from the above survey items (step S103). Specifically, multinomial logistic regression analysis was performed with the burnout scale as the dependent variable. Based on the results, stress score, coping scale, presence/absence of child rearing, presence/absence of night shift, and presence/absence of spouse were extracted from the above survey items as items related to burnout.

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

Next, the first-version algorithm acquired in step S104 was verified (step S105). Specifically, first, the following survey (2016) was conducted using the input device 100 with a nurse in one medical facility as a subject. That is, items for calculating a stress score, a coping scale and a resilience scale, presence/absence of child rearing, presence/absence of night shift, presence/absence of spouse, and work schedule were input from the subject to the input terminal 110. The sleep status is acquired by adding the sleep status as an input item from the input terminal 110 without using the detection result of the wearable device 120. In addition, the same subject was investigated for the burnout scale based on the above-mentioned burnout question item. Then, based on the results of the burnout scale survey using the burnout question items, the subjects were classified into a high group, a depersonalized group, an emotional group, and a low group, as in the burnout evaluation in step S102. On the other hand, based on the results of the survey on the stress score, the coping scale, the presence or absence of child rearing, the presence or absence of night shift, and the presence or absence of a spouse, the first version algorithm acquired in step S104 is applied to the high group, the depersonalized group, and the emotional group. Classification of subjects into groups and low groups was performed. Then, based on the classification result based on the results of the burnout scale investigation using the above-mentioned burnout question item, it was evaluated whether the classification result by the judgment of the algorithm shows the correct content. As a result, the ratio of the number of subjects whose classification results determined by the algorithm 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 in which items regarding resilience scale and sleep were added was created (step S106). The items added here correspond to steps S9 to S13 in FIG. The reason why the resilience scale and the item regarding sleep are added is that the resilience scale and the sleep situation are strongly associated with burnout according to the result of the survey performed in step S104. The branching conditions related to the resilience scale and sleep status in the algorithm were derived based on the resilience scale and the measured sleep time obtained from the survey results.

Next, the algorithm created in step S106 was verified based on the investigation result executed in step S105 (step S107). Specifically, based on the classification result by the burnout scale based on the burnout question item, the result determined based on the algorithm of FIG. 2 was evaluated in the same manner as step S105 of FIG. 3 (implemented in 2017. ). 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 the judgment value derived by the server 300. The third column from the left shows the number of test subjects corresponding to the judgment value. The fourth column from the left shows the number of persons whose classification result corresponding to the judgment value has the correct content. The fifth column from the left shows the number of persons whose classification result corresponding to the judgment value was incorrect. The sixth column from the left shows the discrimination detection rate and corresponds to the ratio of the number of persons in the fourth column to the number of persons in the third column. The seventh column from the left shows the classification error rate and corresponds to the ratio of the number of persons in the fifth column to the number of persons in the third column.

In Table 2, the "burnout group" corresponds to the combined group of the high group, the depersonalized group and the emotional group on the burnout scale. The "risk group" was classified into the low group on the burnout scale in the first version of the algorithm, but the risk of burnout is low compared to the burnout group, but not the low group, but it will move to the burnout group in the future There was a risk group. "Low group" corresponds to the low group on the burnout scale. This group belongs to the range that was classified into the low group in the burnout scale even in the algorithm of the first version, and is a group that is unlikely to be in the burnout state at this time.

In Table 2, whether the classification result is correct when the judgment value is 1 to 10 (4th to 5th columns from the left) is determined by the burnout scale based on the burnout scale based on the burnout question item. Certainly, it is based on whether the content was classified into the high group, the depersonalized group, and the emotional group. In addition, whether the classification result is correct when the judgment value is 100 (4th to 5th columns from the left) is determined by the burnout scale based on the burnout question item. Based on whether or not the content was classified into. As for whether the classification result when the judgment value was 11 to 15 is correct (4th to 5th columns from the left), the burnout scale was certainly low in the survey of the burnout scale based on the burnout question item. Based on whether or not the content was classified into other than the group. Note that the range in which the determination value is 11 to 15 is the range that was classified as the low group in the algorithm of the first edition of step S104, but as described above, there is a risk group at risk of shifting to the burnout group. The range is reclassified as. Therefore, the classification result based on the burnout scale (being a low group) is not necessarily correctly determined in this range.

Next, with respect to a part of the subjects of the survey (2016) conducted in step S105, the determination and the survey by the algorithm of FIG. FIG. 4 shows the transition from 2016 to 2017 in the determination result of the same subject, and also shows the subject's retirement status and intention to leave (wishing to leave or hesitating to leave). In Fig. 4, the numbers below the "2016" column name indicate the 2016 determination result, and below the "2017" column name the 2017 determination result. Show. The arrow from the numerical value of 2016 to the numerical value of 2017 shows how the subject corresponding to each judgment value of 2016 was judged in 2017. For example, the arrow heading from the judgment value 4 in 2016 to the judgment value 1 in 2017 indicates that the judgment value in 2017 in the subject whose judgment value was 4 in 2016 became 1. Each arrow indicates that the thicker the line, the greater the number of subjects corresponding to the transition of the determination value indicated by the arrow. In addition, arrows pointing from the judgment value of 2016 or the judgment value of 2017 to “retirement”, “retirement desire”, and “lost retirement” indicate whether the subject corresponding to each judgment value of 2016 or 2017 actually retired, Indicates that you have come to have a retirement intention or a desire to retire. As shown in FIG. 4, most of the retirees and those who are willing to retire have arisen from the burnout group in the latest judgment, and some have come from the risk group. Retirees and intent to retire did not arise from the lowest group in the most recent decision. Most of the subjects who were judged to have a judgment value of 1 to 10 (burnout group) in 2016 are also judged to have a judgment value of 1 to 10 (burnout group) in 2017. As described above, the reason why the judgment value 10 is classified into the burnout group instead of the risk group is that there are many retirees and those who intend to retire in the judgment values 1 to 10 and the 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 2017 as well.

According to the present embodiment described above, it is possible to directly determine the risk of burnout of the 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 When the sleep is good (step S13: "until 25:30"), the determination value is 100, that is, the risk is determined to be low. As described above, the algorithm of FIG. 2 is determined based on not only the stress level of the subject in the risk determination but also the resilience level and the goodness of sleep. The certainty of is guaranteed.

Further, as shown in the algorithm of FIG. 2, when the average sleep time is 6.8 hours or more and less than 8 hours and the average bedtime is up to 25:30, the risk is determined to be low. That is, if the average sleeping time is too short or too long, the goodness of sleep is low, and the slower the average bedtime is, the lower the goodness of sleep is. The certainty of the determination is secured based on the sleep goodness evaluated in this way.

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

Further, as shown in the algorithm of FIG. 2, the risk of burnout of the subject is determined step by step based on the height of coping, the presence or absence of night shift, the age, the presence or absence of child rearing, and the presence or absence of a spouse. According to this, it is possible to judge the burnout risk stepwise based on various factors related to the burnout risk.

<Modification>
The above is a description of preferred embodiments of the present invention, but the present invention is not limited to the above-described embodiments, and various modifications are possible 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 above-described embodiment, the sleep state of the subject is evaluated based on the average sleep time and the average bedtime on the day before the day shift. However, the 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, the average wake-up time, and the like. Moreover, in the above-described embodiment, the sleep status is evaluated based on whether or not 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, the type of work, and the like. For example, 6 hours or 7.5 hours may be used.

Further, in the above-described embodiment, the wearable device 120 is used to detect the sleep state of the subject. However, the subject can directly input the bedtime, the wake-up time, and the day shift/night shift into the input device 100, and the server 300 or the like averages the average sleeping time and the average day before the day shift based on the input contents. The system 1 may be configured to derive the bedtime.

Moreover, 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 of the wearable device 120. In this case, the input terminal 110 may transmit the bedtime and the wake-up time to the server 300 based on the grasped sleep period. Then, the server 300 calculates the average sleeping time and the average bedtime of the day before the day shift from the bedtime and the wakeup time transmitted from the input terminal 110. The input terminal 110 may calculate the average sleep time or the average bedtime on the day before the day shift and send the calculation result to the server 300.

Further, in the above-described embodiment, as the stress evaluation acquisition unit and the resilience evaluation acquisition unit, the stress score calculation unit 310 and the resilience scale calculation unit 330 are based on the information transmitted from the input device 100, and the stress score and the resilience scale, That is, the evaluation result of the height of stress and the height of resilience is calculated. However, as a means for acquiring the evaluation result of the height of stress and the height of resilience, other means may be provided in the server 300. For example, the server 300 may be provided with means for receiving the stress score and the resilience measure calculated or input in another device through the communication network. Further, the server 300 may be provided with means for directly inputting the stress score and the resilience scale.

Further, in the above-described embodiment, as the sleep evaluation acquisition means, the sleep evaluation unit 351 calculates the average sleep time and the average bedtime of the day before the day shift, and evaluates the goodness of sleep. However, as a means for acquiring the evaluation result of the sleep condition, means of other modes may be provided in the server 300. For example, the server 300 may be provided with means for receiving the average sleep time and the average bedtime of the day before the day shift calculated or input in another device through the communication network. Further, the server 300 may be provided with means for directly inputting the average sleep time and the average bedtime of the day before the day shift.

In addition, the algorithm of FIG. 2 according to the above-described embodiment is created based on a survey targeting nurses. However, an algorithm created for workers of other occupations may be used. Also in this case, a decision tree including three items of high stress, high resilience, and good sleep is used to improve the certainty of determining the risk of burnout. Moreover, as the question items for evaluating the height of stress and resilience, items different from those in the above-described embodiment may be used. For example, “CFSI: Cumulative fatigue symptom index (Koshikawa, 1990)” may be used as a question item for evaluating the level of stress. In addition, “two-dimensional resilience scale (Hirano, 2010)” may be used as a question item for evaluating the height 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 Situation Acquisition Unit 350 Risk Evaluation Unit 351 Sleep Evaluation Unit

Claims (6)

A stress evaluation acquisition means for acquiring an evaluation result regarding the stress level of the subject,
Resilience evaluation acquisition means for acquiring the evaluation result regarding the height of the resilience of the subject,
Sleep evaluation acquisition means for acquiring an evaluation result regarding the goodness of sleep of the subject, and
Based on at least the evaluation results acquired by the stress evaluation acquisition means, the resilience evaluation acquisition means and the sleep evaluation acquisition means, if the stress is low, the resilience is high, and the sleep is good, the burnout of the subject is performed. A program for judging a working condition, characterized by causing a computer to function as a burnout risk judging means for judging that the risk is low. The sleep evaluation means evaluates that sleep is good when the average sleep time per day of the subject is within a range from a predetermined lower limit to an upper limit, and when the average bedtime of the subject's day before work is early. The program for determining a working condition according to claim 1. The burnout risk determination means,
Even if the subject's stress is low, it is determined that the subject's risk of burnout is high if the subject's resilience is low. program. The burnout risk determination means,
The risk of burnout of a subject is determined stepwise based on at least one of the height of coping, presence or absence of night shift, height of age, presence or absence of child rearing, and presence or absence of spouse. The program for judging the working condition described in 3. The 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 stress level of the subject,
Resilience item input means for inputting answers to question items for deriving the height of the resilience of the subject,
A detection means for detecting the sleep condition of the subject,
The stress evaluation acquisition means derives the stress level of the subject based on the input content by the stress item input means,
The resilience evaluation acquisition means derives the height of the resilience of the subject based on the input content by the resilience item input means,
A work state determination system, wherein the sleep evaluation acquisition unit derives a goodness of sleep of a subject based on a detection result by the detection unit. A stress evaluation acquisition means for acquiring an evaluation result regarding the height of the stress of the subject,
Resilience evaluation acquisition means for acquiring the evaluation result regarding the height of the resilience of the subject,
Sleep evaluation acquisition means for acquiring an evaluation result regarding the goodness of sleep of the subject,
Based on at least the evaluation results acquired by the stress evaluation acquisition means, the resilience evaluation acquisition means and the sleep evaluation acquisition means, if the stress is low, the resilience is high, and the sleep is good, the burnout of the subject is performed. And a burnout risk determining means for determining that the risk of the worker is low.

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