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- Original Article Development of standard job classification codes for building a job-exposure matrix for police officers
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Sangjun Choi1,2,*
, Ju-Hyun Park3, Inah Kim4, Jungwon Jang4, Jeehee Min5, Sang Baek Koh6, Seongwon Kim7, Yeji Sung1, Kyoung Yoon Ko1, Su Min Oh1, Un-Yeol Jeon3 -
Annals of Occupational and Environmental Medicine 2025;37:e10.
DOI: https://doi.org/10.35371/aoem.2025.37.e10
Published online: April 28, 2025
1Graduate School of Public Health and Healthcare Management, The Catholic University of Korea, Seoul, Korea
2Catholic Institute for Public Health and Healthcare Management, Seoul, Korea
3Department of Statistics, Dongguk University, Seoul, Korea
4Department of Occupational and Environmental Medicine, Hanyang University College of Medicine, Seoul, Korea
5Department of Occupational and Environmental Medicine, Hanyang University Hospital, Seoul, Korea
6Department of Preventive Medicine, Institute of Occupational and Environmental Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
7Future-Academic Cooperation Foundation, Yonsei Wonju Health System, Wonju, Korea
- *Corresponding author: Sangjun Choi Graduate School of Public Health and Healthcare Management, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea E-mail: junilane@gmail.com
• Received: December 24, 2024 • Revised: April 19, 2025 • Accepted: April 24, 2025
© 2025 Korean Society of Occupational & Environmental Medicine
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
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Background This study aimed to develop standard job categories for constructing a job-exposure matrix (JEM) for police officers in South Korea and to evaluate their applicability.
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Methods We examined standard job codes related to police personnel management and compared them with job classifications from police publications. Using R Shiny, we developed a web-based search tool for standard codes. A pilot survey of 130 police officers assessed the codes' applicability and relevance to health-related hazardous factors.
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Results Eighty-seven standard functional codes used in the police personnel management system POOL were organized into minor categories as the basic units of standard jobs. These were grouped into 20 sub-major categories and further consolidated into 10 major categories to develop the standard job codes. The responses to the standard job codes in the pilot survey were 75% accurate compared with the final expert evaluation results and 99.2% accurate compared with the algorithm-based automatic allocation results. The results of the job-hazardous factor network analysis revealed that the most frequently reported hazardous factor was emotional labor, followed by night shifts and electromagnetic waves. Emotional labor was identified as the top hazardous factor in six out of the nine standard job categories.
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Conclusions The standard job codes developed in this study were designed in connection with the personnel management system for police officers, making them well-suited for constructing a comprehensive JEM for the entire police force.
BACKGROUND
Police officers are vital for law enforcement, focusing on crime prevention and social order. They are also first responders, alongside firefighters and medical personnel, addressing critical situations.1 Police officers frequently face physical and chemical hazards while performing their duties without proper protection measures.2-6 It has been reported that police officers face a high risk of developing occupational post-traumatic stress disorder (PTSD), not only due to physical hazards such as violence encountered during duty but also from their experiences investigating horrific incidents like murder scenes.7 Moreover, the nature of police work, which involves frequent shift work and night shifts, along with prolonged exposure to emotional labor through public-facing duties and physical and chemical hazards, has been reported in both domestic and international studies to increase the risk of cardiovascular diseases8-12 as well as cancer.13-19
Since 2004, the United Kingdom has conducted a large-scale cohort study, the Airwave Health Monitoring Study (AHMS), targeting police officers.20 The AHMS initiated an investigation into the health effects of using TETRA (Terrestrial Trunked Radio), a communication device utilized by police officers.21 The study includes 50,000 police officers, collecting physical measurements, blood and DNA samples, lifestyle data, health surveys, and analyzing health risks and their association factors.22,23 The 2022 addition of the “Special Health Examination” clause to the “Basic Law on Health, Safety, and Welfare of Police Officers” institutionalized special exams, yet police occupational health management remains a blind spot in Korea. Thus, it is essential to create a system for assessing and managing hazardous risk factors, considering the job characteristics of all police officers.
To create an effective safety and health management system for police officers, exploring, gathering, and evaluating data regarding job-specific hazardous risk factors is vital. Nonetheless, inconsistencies arise in job classification due to the absence of standardized criteria in publications from the National Police Agency (NPA) and related research. Recently, the NPA has been conducting a five-year research project since 2022 to develop an “Intelligent Big Data Integrated Platform for Personalized Health Management Services for Police Officers.” The authors of this paper have engaged in research to identify risk factors for all police officers based on their jobs and to develop a job-exposure matrix (JEM) for management, while also working to standardize police officer roles. This paper presents the outcomes of standard job classification codes that can inform future JEM development for police officers. It evaluates the use of these job codes in understanding exposure characteristics to hazards based on a pilot survey of several police officers.
METHODS
Three principles guided the creation of police job codes. First, inclusiveness ensures applicability to all officers, supporting health management system for the force. Second, connectivity allows linking with various datasets on officer training, welfare, and health. Finally, continuity ensures adaptability to evolving information, maintaining relevance. These principles were integrated into the development for a robust framework for police job codes classification.
To develop job codes that meet these principles, the first step was to examine the personnel management system for police officers. This involved interviews with relevant personnel and gathering standard code data from the NPA for thorough analysis. Second, the ‘Key competency guidebook for police duties,’24 which outlines core competencies for police jobs, and the ‘2020 Police Statistics Yearbook,’25 periodically published by the NPA, were reviewed. Third, we examined the organizational structure and department classifications on the official NPA and subordinate police websites.
Organizational names in major, sub-major, and minor sections of the personnel management system, NPA publications, and charts were compiled into a spreadsheet. Common functional terms for job roles were identified. Final job codes were organized into a classification system. Related index keywords were created in a database by extracting terms from subordinate job categories and relevant task descriptions from the reviewed materials.
To help users select standard codes intended for the standard job codes developed in this study, a search tool called the ‘Police Function Code Finder (PFF)’ was created. This tool was built using Shiny,26 an R package that aids in creating interactive web applications, allowing users to access and navigate the codes easily. The term ‘function code’ was used in the title of the search tool because the word ‘function’ is more commonly employed than ‘job’ in police organizations, making it a more familiar term for police officers.
The PFF serves as a search algorithm, providing descriptions and a list of standard codes for sentence-based searches. It evaluates how well inputted “words in a sentence.” match index keywords associated with job codes. Match scores are as follows: matches with the standard code name earn 2 points, core keyword matches receive 1 point, and additional keyword matches gain 0.5 points. Codes are displayed in descending order based on scores. For ties, rankings are determined by the number of core keyword matches; those with more matches appear higher.
Survey structure and overview
A pilot survey tested the applicability of new standard job codes. Its main goal was to evaluate how well these codes could categorize police officers' roles and exposures to hazards. The questionnaire explored work conditions, health status, medical utilization, health behaviors, job stress, and mental health, based on job history using the created standard job codes for future police officers.
Job-related characteristics were evaluated through questions about the organization, department, function (participants chose from standard job codes), years of service, work type, and workplace. The survey also included questions on health hazards linked to their tasks. In the survey, participants were given brief descriptions of 22 hazardous factors presented in Supplementary Table 1, encompassing physical, chemical, biological, ergonomic, and psychosocial risks. They were asked to select all factors they believed could affect health in relation to the tasks performed in their respective departments. Participants provided information about their organization and job history. The survey was conducted via an app developed for mobile and computer responses, administered from April to June 2024, targeting police officers from the Wonju Police Station in Gangwon Province.
Evaluation of the accuracy of standard job assignment
The agreement rate was analyzed by comparing the standard job codes selected by respondents (JobRespondents) in the pilot survey with those assigned by experts (JobExpert) who developed standard job codes. These experts assigned the codes based on respondents' organizational and departmental information.
Furthermore, to integrate existing police officer data previously recorded with non-standardized job information into JEM using standard job codes, it is essential to enable reallocation to standard codes. An algorithm was developed using R27 to automatically assign major categories of the standard job codes based on the organization, department, and function information collected in the survey. The algorithm assigns scores: 10 points for code name matches, 5 for core keywords, and 1 for additional keywords. The job code with the highest score is assigned automatically. The agreement rate between expert (JobExpert) and automatic (JobAlgorithm) assignments evaluates the feasibility of automation, with higher rates indicating better reliability for standard job code assignments.
The pilot survey applying the standard job codes was distributed to 300 participants, and the responses from 130 individuals who completed the survey, including job-related questions, were analyzed. Using the finalized major category standard job codes reviewed by experts, the frequency distribution by work type (shift work, on-call duty, day shift) and workplace (indoor, outdoor) was analyzed. Additionally, a heat map of the percentile frequencies for hazardous factors identified as impacting health was created using Tableau Desktop (2024 Tableau Software, Salesforce).28 The network visualization of hazardous factors selected as impacting health for each standard job code was analyzed using Gephi (0.10).29
This study was approved by the Institutional Review Board (IRB) of Hanyang University (IRB No. HYUIRB-202308-013-1 and HYUIRB-202009-014-7). All participants provided written informed consent prior to participation in the study.
RESULTS
The management of police personnel utilizes two systems, as detailed in Table 1. The first system is “e-Saram," a standardized electronic human resource management platform aimed at supporting personnel administration and policy functions across the government, thereby optimizing the entire personnel process from recruitment to retirement.30
When a new organization forms in the police force, the Ministry of Personnel Management creates and sends a new code to the NPA, which assigns a departmental code by appending a serial number to the parent organization’s code, like headquarters or division. This process lacks standardized regulations. For police officers, the e-Saram system has operated since 2005, leaving those who joined before 2005 with restricted access to information. Additionally, since the system covers all public officials, filtering data for police officers is challenging, complicating evaluations of the police's current state workforce.
The second system, “POOL,” is a personnel management system used by the NPA. It matches organizational codes from the e-Saram system with those created in POOL to prevent duplication, and assigns function codes based on job characteristics. POOL includes code information for all police officers since 1940 (excluding National Forensic Service and general administrative roles). This extensive coverage makes POOL’s codes more effective than e-Saram for creating a JEM for all current and former police officers in the future.
In May 2023, we acquired codes from the e-Saram system and POOL function codes from the NPA for analysis, uncovering 87,575 total codes. Of these, 22,601 were fully matched and used across all three systems. After removing duplicates, we finalized 22,413 unique codes.
Fig. 1 illustrates each code's structure. The POOL code includes four two-digit subcategories denoting hierarchical levels: provincial/metropolitan police agency, station/bureau, division, and section/team. In contrast, the e-Saram code has 15 digits but lacks a consistent pattern, even for similar organizations. Conversely, the POOL code unifies similar names into a single designation with uniform numbering. For example, in Fig. 1, the e-Saram organizational names are presented as “Police agency - Seoul Metropolitan agency - Seongbuk station - General Affairs division - Accounting” and “Police agency - Seoul Metropolitan agency - Seongbuk station - General Affairs division - Accounting section.” While the only difference is in the final terms "Accounting" and “Accounting section,” e-Saram assigns different code numbers to each. In contrast, POOL standardizes these as “Seoul agency - Seongbuk - General Affairs - Accounting” under four subcategories and assigns the same code number, “41171112.”
Fig. 1 shows that function codes effectively represent job characteristics. Composed of two letters, these codes reveal similarities when sharing the first character. Thus, they serve as standard job codes for JEM construction. After matching with organizational codes, removing duplicates, and consolidating identical names, 87 of the original 135 function codes were extracted, comprising the minor standard job group codes. Duplicate names were selected based on their usage frequency with organizational codes, relevance to similar codes, and classification in a sub-major group.
The first character of the minor group codes classifies them into 20 sub-major groups, which are categorized into 10 major groups based on organizational structure and functions. Table 2 summarizes the final classification of the standard job codes. For additional details on Korean code names and their descriptions, visit the web-based tool, PFF, using the link below: https://kscf.shinyapps.io/pff_app/.
The PFF extracted sub-major and minor group code names linked to major group codes to create core keywords representing those codes. It also generated supplementary keyword databases by extracting organizational names from the documents and structures from the NPA's website. Users can search for the standard code list with keywords for major group codes by entering job-related phrases. The system provides scores indicating the alignment between the entered phrases and keywords, displaying the standard codes in descending order of total scores.
The distribution of job categories and work experience among the 130 pilot survey respondents is presented in Supplementary Table 2. The results of the standard jobs selected by participants for their current or past work history (JobSurvey), the outcomes assigned by the automated algorithm (JobAlgorithm), and the final evaluation results from experts (JobExpert) are summarized in Table 3. Based on the final expert evaluation results, the agreement rate with the survey responses was 75.4%. Among job categories, mobile/special forces showed the lowest agreement rate at 36.8%, followed by community police at 57.8%. The agreement rate for the automated assignment results was 99.2%, confirming the feasibility of automatic standard job assignment based on departmental information.
Fig. 2 illustrates the distribution of work types and workplaces for nine standard jobs finalized by experts. Community police had the highest shift work rate at 93%, while intelligence, security, and foreign affairs had the highest day work rate, with 96% of respondents reporting day work work. Jobs were classified based on the primary workplace into desk (indoor) and field (outdoor) jobs. Community police had the highest proportion of field jobs at 94%, while administration had the highest desk jobs at 89% work.
Fig. 3 displays a heat map of hazardous factors affecting health by job type. Solid red boxes highlight the most frequently reported (1st rank) hazards, while dotted boxes denote second most reported (2nd rank) hazards. Among nine job groups, six in public-facing roles identified emotional labor as the top hazard.
Fig. 4 shows the network visualization analysis of job-hazard connections. More arrows linked to a hazardous factor indicate more associated jobs, and thicker arrows represent higher response frequency. Emotional labor was the most frequently selected hazard, followed by night work, electromagnetic waves, and shift work.
DISCUSSION
Job information is essential for assessing workplace exposure in epidemiological studies. Workers in similar roles face comparable exposures, regardless of location. This has led to JEMs, which identify workplace exposures and allow evaluation of individual exposures without detailed job or environment data.31 Utilizing standardized job group information within a specific industry enables the development of more detailed JEMs.32 Furthermore, if the task characteristics of each job group can be more specifically identified, it becomes possible to construct task-based JEMs.33 For police officers, several systematic reviews have examined potential hazards and risk factors,34,35 yet studies focused on developing standardized job group-specific JEMs for the entire police force remain limited. Thus, as the first step in constructing a JEM for police officers, this study aimed to establish standard job codes that account for the characteristics of different job groups.
The job classifications used in the official publications issued by the NPA are neither standardized nor consistent. In this study, we propose a standardized job code system based on the 87 function codes used in the POOL personnel management system for police officers. This system comprises 20 sub-major categories and 10 major categories. The 10 major categories are deemed highly practical as they either encompass or closely align with the classification items in existing publications by the NPA. For example, precinct stations and police substations, which make up the largest portion of the total police workforce, are classified as “community police” in this study. This classification is considered appropriate, as Korea’s NPA regulation titled “Rules on the Organization and Operation of Community Police” (NPA Regulation No. 602) defines community police as “precinct stations and police substations.” Furthermore, police officers working in substations, smaller units established to manage localized security within the jurisdiction of police stations and precincts, also fall under the category of community police. Therefore, the term “community police” is more fitting than “precinct station and police substation.”
Additionally, the “Rules on the Operation of Precincts and Substations” explicitly mention “community police activities” as part of the precinct’s responsibilities. These activities are described as “efforts to prevent crime and safety incidents while incorporating public concerns and feedback into security operations through cooperation with local residents, institutions, and organizations.” This further strengthens the validity of using “community police” as a classification name.
A survey was conducted with 130 officers to assess if frontline police officers can correctly identify their job (function) codes under the proposed standard job code system. The agreement rate between the officers’ survey responses and the standard job codes assigned after expert evaluation was 75.4% (Table 3). The mobile/special forces showed the lowest agreement rate among the job categories. Upon reviewing the free-text inputs provided by officers who selected “other” or entered their codes manually, it was discovered that many chose “security (D),” a major category in the standard job code system, with responses like “security,” “security operations,” and “patrol.” Others specified positions such as “deputy platoon leader,” “platoon adjutant,” “platoon leader,” or “team member.” This is because the duties performed by the mobile/special force share characteristics with those of security, highlighting the need for a clearer explanation of the distinction between security and mobile/special force in the standard job codes in the future. Similarly, for community police, which also showed a lower agreement rate, the most common selection was “patrol” under the security (D) category, with 31 respondents choosing this option, followed by “community safety” with five respondents. This can be interpreted as stemming from the fact that the duties of community police (M) encompass elements of patrol and community safety. As with the mobile/special force (OP) category, it was deemed necessary to clearly differentiate between community police (M), security (D), and community safety (B) in the standard job codes.
In occupational health research, the automatic assignment of standardized job codes from free-text job descriptions is essential for the target population. Tools for standardized job coding have been created to process data in English as well as multiple other languages.36-38 Similarly, in this study, we developed and implemented an algorithm that automatically codes standardized job codes by evaluating the degree of match between respondents’ input regarding their affiliated department and function and the keywords of the standardized job codes. The resulting agreement rate was extremely high at 99.2%, indicating that even if police officers did not select the correct standard code during the survey, it is possible to allocate the appropriate standard job code using the information they provided about their department and function.
In some job categories, the auto-assignment algorithm showed lower accuracy, indicating a need for improvement. For example, in Table 3, the accuracy for “Protective service (D)” was 70%, with cases incorrectly assigned that were actually “Administration,” “Traffic,” or “Intelligence Security and Foreign Affairs” based on expert review. The algorithm matched user responses (e.g., department name, manually entered info) with job code titles and keywords. However, overlapping keywords—such as “traffic” appearing in both “Protective service (D)” and “Traffic (W)”—led to misclassification. To improve accuracy, the keyword overlaps across job groups should be reviewed and refined. Auto-assignment was not possible in cases with insufficient or non-matching input data. Of the 16 unassigned cases, eight had incomplete responses that could not be evaluated by experts, while the other eight included partial information that did not match existing keywords—some using abbreviations or informal terms. To improve assignment accuracy and coverage, future refinements should include expanding the keyword set to incorporate common variations and abbreviations. Nevertheless, if relevant information such as department or function is available, it is expected that existing police-related data not previously classified under standard job codes can be assigned through the improved auto-assignment method and effectively utilized for future JEM construction.
An analysis of hazardous factors affecting health across job categories revealed that emotional labor was the primary hazard identified by various job groups (Fig. 3) and ranked highest in overall response frequency (Fig. 4). A meta-analysis of mental health outcomes among police personnel, which included 60 cross-sectional studies, seven longitudinal studies, and a total of 272,463 police officers worldwide, reported the following prevalence rates: depression (14.6%), PTSD (14.2%), generalized anxiety disorder (9.6%), suicidal ideation (8.5%), alcohol dependence (5.0%), and hazardous drinking (25.7%).35 This indicates that mental health issues among police officers are over twice as common as in the general population, emphasizing them as a significant health concern that demands immediate attention. The second most selected hazardous factor was working at night. Studies examining the relationship between shift work and job stress among police officers have found that those working afternoon and night shifts experience significantly higher levels of stress than officers working daytime shifts.39 The third major hazard was electromagnetic waves, reflecting concerns about exposure risks from frequent radio use. The ongoing AHMS cohort study in the United Kingdom, started in 2004, included a 2018 epidemiological study on radio use and cancer risk, concluding no significant increased cancer risk for police officers using radios. It emphasized the need for ongoing longitudinal research.22 In South Korea, it's essential to start with a baseline survey on police officers' radio usage patterns.
This study showed that standard job codes can be used to categorize the traits of police jobs and assess the connection between these jobs and risk factors via a pilot survey. However, limitations exist in interpreting the results as indicative of all standard job categories due to the limited number of respondents (130) and the disproportionate representation of community police officers (116) respondents.
Although the survey included additional items related to potential health effects—such as sleep health, depressive symptoms, anxiety, PTSD, experiences of emotional labor/workplace violence, job stress, burnout, and working conditions—these were not analyzed in the present study. Given the small sample size, we determined that it would be inappropriate to use this dataset to construct a JEM or draw conclusions regarding health effects. A comprehensive survey aimed at the entire police force is scheduled for the future, enabling a more precise evaluation of the potential exposure to hazardous factors and the health impacts associated with each job category.
CONCLUSIONS
This study develops standard job codes for a JEM that identifies job-exposure characteristics of police officers in South Korea. Analyzing personnel management data and publications created 20 sub-major and 10 major job code categories from 87 function codes. A pilot survey with 130 police officers tested the feasibility of automatic assignment. Results indicated a need to clarify classification criteria for mobile/special forces versus community police and security. The study confirmed that standard job codes could be automatically assigned with 99.2% accuracy using officers' data. It also examined the link between categorized jobs and hazardous exposures, suggesting these codes can create a comprehensive JEM for all police officers in the future.
Abbreviations
AHMS
Airwave Health Monitoring Study
JEM
job-exposure matrix
NPA
National Police Agency
PFF
Police Function Code Finder
PTSD
post-traumatic stress disorder
TETRA
Terrestrial Trunked Radio
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Funding
This work was supported by Smart Health Care Program (www.kipot.or.kr) funded by the Korean National Police Agency (KNPA, Korea) [Project Name: Development of an Intelligent Big Data Integrated Platform for Police Officers’ Personalized Healthcare / Project Number: 220222M01].
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Competing interests
Inah Kima and Sang Baek Koh contributing editors of the Annals of Occupational and Environmental Medicine, were not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.
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Author contributions
Conceptualization: Choi S, Park JH, Kim I, Jang J, Min J, Koh SB, Kim S. Data curation: Choi S, Park JH, Jang J, Min J, Kim S, Sung Y, Ko KY, Oh SM, Jeon UY. Formal analysis: Choi S, Park JH, Jang J, Min J. Investigation: Choi S, Kim I, Jang J, Koh SB, Kim S, Sung Y, Ko KY, Oh SM, Jeon UY. Methodology: Choi S, Park JH, Kim I, Jang J, Min J. Validation: Park JH, Kim I, Koh SB. Visualization: Choi S, Sung Y, Oh SM, Ko KY, Jeon UY. Writing - original draft: Choi S. Writing - review & editing: Park JH, Kim I, Jang J, Min J, Koh SB, Kim S.
NOTES
SUPPLEMENTARY MATERIAL
Supplementary Table 1.
Response options for inquiries regarding health-related hazardous factors.
Supplementary Table 2.
Number and work experience of pilot survey respondents by job category and workplace characteristics.
Fig. 1.
Example of data linking organizational and functional codes between POOL and e-Saram. aPOOL: Personnel management system developed by the Korean National Police Agency; be-Saram: Electronic personnel management system developed by the Ministry of Personnel Management.
Fig. 2.
Distribution of work types (left side) and working places (right side) by standard job categories among pilot survey respondents (total n = 130).
Fig. 3.
Heatmap of percentile frequencies of hazardous factors reported to affect health by job category (solid red box: primary hazardous factor, dashed red box: secondary hazardous factor).
Fig. 4.
Visualization of the network between job categories and health hazardous factors (The numbers indicate the ranks of hazardous factors selected with high frequency).
Table 1.
Comparison of police personnel management system
Table 2.
List of standard job codes
|
Major |
Sub-major |
Minor |
No. of recordsa |
|||
|---|---|---|---|---|---|---|
| Code | Name | Code | Name | Code | Name | |
| ANT | Administration | A | Police affairs | AA | Police affairs | 1,141 |
| AB | General affairs | 5 | ||||
| AC | Welfare | 10 | ||||
| AD | Accounting | 527 | ||||
| AE | Civil complaints | 10 | ||||
| AF | Personnel education | 11 | ||||
| AG | Personnel | 19 | ||||
| AI | Education | 10 | ||||
| AJ | Equipment | 43 | ||||
| AK | Planning | 17 | ||||
| AL | Budget | 6 | ||||
| AM | Legal affairs | 5 | ||||
| AO | Public relations | 85 | ||||
| AQ | Planning and budget | 11 | ||||
| N | Chief | NA | Chief | 1 | ||
| T | Public safety | TA | Public safety | 23 | ||
| TB | Police affairs planning | 34 | ||||
| TE | Public safety intelligence | 80 | ||||
| B | Community safety | B | Community safety | BA | Community safety | 1,421 |
| BC | Community safety and traffic | 120 | ||||
| BD | Women and juveniles | 974 | ||||
| BE | 112 emergency response | 475 | ||||
| C | Investigation | C | Investigation | CA | Investigation | 1,490 |
| Ca | Women and juvenile investigation | 426 | ||||
| CB | Inquiry | 2 | ||||
| CD | Cyber | 125 | ||||
| CE | Criminal investigation | 746 | ||||
| CF | Mobile investigation unit | 22 | ||||
| CG | Narcotics | 2 | ||||
| CJ | Investigation support | 397 | ||||
| CK | Detention management | 140 | ||||
| CL | Intelligence crime investigation | 461 | ||||
| CM | Major crime investigation | 317 | ||||
| CN | Forensic science | 27 | ||||
| CO | Criminal investigation support | 251 | ||||
| CQ | Community crime investigation | 73 | ||||
| CR | Economic crime investigation | 361 | ||||
| CS | Case management | 2 | ||||
| CU | Anti-corruption and public crime investigation unit | 3 | ||||
| CY | Security investigation | 86 | ||||
| CZ | Traffic crime investigation | 236 | ||||
| D | Protective service | D | Protective service | DC | Protective service | 329 |
| DD | Operations | 1 | ||||
| DE | Aviation | 4 | ||||
| DG | Protection | 47 | ||||
| DH | Security and traffic | 358 | ||||
| DI | Auxiliary police | 18 | ||||
| DJ | Security situation | 6 | ||||
| DL | Security operations | 411 | ||||
| DM | Crime prevention patrol | 81 | ||||
| EFGHV | Intelligence security and foreign affairs | E | Intelligence | EA | Intelligence | 175 |
| EB | Security | 41 | ||||
| EC | Information security | 4 | ||||
| EF | Security and foreign affairs | 7 | ||||
| EG | Information security and foreign affairs | 234 | ||||
| EH | Public safety intelligence and foreign affairs | 90 | ||||
| F | Foreign affairs | FA | Foreign affairs | 237 | ||
| G | Audit | GA | Audit | 750 | ||
| GB | Inspection | 9 | ||||
| H | Information and communications | HA | Information and communications | 18 | ||
| HD | Information equipment | 415 | ||||
| V | National security | VA | National security | 254 | ||
| I | Academy | I | Academy | IB | Police university administration | 45 |
| IC | Police university instructor | 13 | ||||
| IE | General administration | 18 | ||||
| IH | Central administration | 11 | ||||
| II | Central instructor | 7 | ||||
| M | Community police | M | Community police | MA | Police substation | 3,020 |
| MB | Security center | 1,931 | ||||
| MC | Precinct station | 1,159 | ||||
| MD | Post, check | 6 | ||||
| OP | Mobile/Special forces | O | Mobile unit | OA | Mobile unit | 678 |
| OB | Crime prevention patrol unit | 5 | ||||
| OC | Riot police unit | 37 | ||||
| P | Special forces | PA | Special forces | 52 | ||
| W | Traffic | W | Traffic | WA | Traffic | 135 |
| WB | Traffic planning | 5 | ||||
| WC | Traffic safety | 216 | ||||
| WD | traffic operations | 4 | ||||
| WE | Driver's license | 3 | ||||
| WF | Traffic patrol | 13 | ||||
| WG | Traffic investigation | 692 | ||||
| WH | Traffic management | 512 | ||||
| KLRS | Other | K | National forensic service | KA | National forensic service | 30 |
| L | Airport | LA | Airport | 31 | ||
| R | Hospital | RG | Hospital | 90 | ||
| S | Local autonomy | SA | Local autonomy | 16 | ||
Table 3.
Comparison of standard job response results from the pilot survey with algorithm-based automatic allocation and final expert evaluation results
| Standard job | Assigned by expert (JobExpert) |
Assigned by respondents (JobSurvey) |
Automatically assigned by algorithm (JobAlgorithm) |
||
|---|---|---|---|---|---|
| No. | Agreement ratea (%) | No. | Agreement rate (%) | ||
| Administration | 19 | 20 | 94.7 | 24 | 94.7 |
| Community safety | 37 | 26 | 62.2 | 32 | 86.5 |
| Investigation | 37 | 29 | 78.4 | 37 | 100.0 |
| Protective service | 10 | 46 | 90.0 | 13 | 70.0 |
| Intelligence security and foreign affairs | 28 | 21 | 75.0 | 23 | 78.6 |
| Academy | 2 | 0 | 0.0 | 3 | 100.0 |
| Community police | 116 | 67 | 57.8 | 115 | 99.1 |
| Mobile/special forces | 19 | 7 | 36.8 | 20 | 84.2 |
| Traffic | 25 | 22 | 84.0 | 26 | 96.0 |
| Not assigned | 8 | 50 | 0.0 | 16 | 0.0 |
| Total | 130 | 130 | 75.4 | 130 | 99.2 |
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XML DownloadDevelopment of standard job classification codes for building a job-exposure matrix for police officers
Fig. 1. Example of data linking organizational and functional codes between POOL and e-Saram. aPOOL: Personnel management system developed by the Korean National Police Agency; be-Saram: Electronic personnel management system developed by the Ministry of Personnel Management.
Fig. 2. Distribution of work types (left side) and working places (right side) by standard job categories among pilot survey respondents (total n = 130).
Fig. 3. Heatmap of percentile frequencies of hazardous factors reported to affect health by job category (solid red box: primary hazardous factor, dashed red box: secondary hazardous factor).
Fig. 4. Visualization of the network between job categories and health hazardous factors (The numbers indicate the ranks of hazardous factors selected with high frequency).
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Development of standard job classification codes for building a job-exposure matrix for police officers
| Item | e-Saram | POOL |
|---|---|---|
| Development organization | Ministry of Personnel Management | National Police Agency |
| Purpose | Facilitate government-wide personnel administration and streamline processes from recruitment to retirement | Manage police-specific personnel data and assign job-related function codes |
| Key features | Integrates recruitment, payroll, performance evaluations, and training for all public officials; in operation since 2005 for police officers | Includes all police officers since 1940; assigns function codes reflecting job characteristics; matches with e-Saram organizational codes |
| Limitations | Cannot access data for officers who joined before 2005; challenges in filtering police-specific data; lacks detailed standardization for police jobs | Excludes employees from National Forensic Service and general administrative positions; not integrated with government-wide systems |
| Major |
Sub-major |
Minor |
No. of records |
|||
|---|---|---|---|---|---|---|
| Code | Name | Code | Name | Code | Name | |
| ANT | Administration | A | Police affairs | AA | Police affairs | 1,141 |
| AB | General affairs | 5 | ||||
| AC | Welfare | 10 | ||||
| AD | Accounting | 527 | ||||
| AE | Civil complaints | 10 | ||||
| AF | Personnel education | 11 | ||||
| AG | Personnel | 19 | ||||
| AI | Education | 10 | ||||
| AJ | Equipment | 43 | ||||
| AK | Planning | 17 | ||||
| AL | Budget | 6 | ||||
| AM | Legal affairs | 5 | ||||
| AO | Public relations | 85 | ||||
| AQ | Planning and budget | 11 | ||||
| N | Chief | NA | Chief | 1 | ||
| T | Public safety | TA | Public safety | 23 | ||
| TB | Police affairs planning | 34 | ||||
| TE | Public safety intelligence | 80 | ||||
| B | Community safety | B | Community safety | BA | Community safety | 1,421 |
| BC | Community safety and traffic | 120 | ||||
| BD | Women and juveniles | 974 | ||||
| BE | 112 emergency response | 475 | ||||
| C | Investigation | C | Investigation | CA | Investigation | 1,490 |
| Ca | Women and juvenile investigation | 426 | ||||
| CB | Inquiry | 2 | ||||
| CD | Cyber | 125 | ||||
| CE | Criminal investigation | 746 | ||||
| CF | Mobile investigation unit | 22 | ||||
| CG | Narcotics | 2 | ||||
| CJ | Investigation support | 397 | ||||
| CK | Detention management | 140 | ||||
| CL | Intelligence crime investigation | 461 | ||||
| CM | Major crime investigation | 317 | ||||
| CN | Forensic science | 27 | ||||
| CO | Criminal investigation support | 251 | ||||
| CQ | Community crime investigation | 73 | ||||
| CR | Economic crime investigation | 361 | ||||
| CS | Case management | 2 | ||||
| CU | Anti-corruption and public crime investigation unit | 3 | ||||
| CY | Security investigation | 86 | ||||
| CZ | Traffic crime investigation | 236 | ||||
| D | Protective service | D | Protective service | DC | Protective service | 329 |
| DD | Operations | 1 | ||||
| DE | Aviation | 4 | ||||
| DG | Protection | 47 | ||||
| DH | Security and traffic | 358 | ||||
| DI | Auxiliary police | 18 | ||||
| DJ | Security situation | 6 | ||||
| DL | Security operations | 411 | ||||
| DM | Crime prevention patrol | 81 | ||||
| EFGHV | Intelligence security and foreign affairs | E | Intelligence | EA | Intelligence | 175 |
| EB | Security | 41 | ||||
| EC | Information security | 4 | ||||
| EF | Security and foreign affairs | 7 | ||||
| EG | Information security and foreign affairs | 234 | ||||
| EH | Public safety intelligence and foreign affairs | 90 | ||||
| F | Foreign affairs | FA | Foreign affairs | 237 | ||
| G | Audit | GA | Audit | 750 | ||
| GB | Inspection | 9 | ||||
| H | Information and communications | HA | Information and communications | 18 | ||
| HD | Information equipment | 415 | ||||
| V | National security | VA | National security | 254 | ||
| I | Academy | I | Academy | IB | Police university administration | 45 |
| IC | Police university instructor | 13 | ||||
| IE | General administration | 18 | ||||
| IH | Central administration | 11 | ||||
| II | Central instructor | 7 | ||||
| M | Community police | M | Community police | MA | Police substation | 3,020 |
| MB | Security center | 1,931 | ||||
| MC | Precinct station | 1,159 | ||||
| MD | Post, check | 6 | ||||
| OP | Mobile/Special forces | O | Mobile unit | OA | Mobile unit | 678 |
| OB | Crime prevention patrol unit | 5 | ||||
| OC | Riot police unit | 37 | ||||
| P | Special forces | PA | Special forces | 52 | ||
| W | Traffic | W | Traffic | WA | Traffic | 135 |
| WB | Traffic planning | 5 | ||||
| WC | Traffic safety | 216 | ||||
| WD | traffic operations | 4 | ||||
| WE | Driver's license | 3 | ||||
| WF | Traffic patrol | 13 | ||||
| WG | Traffic investigation | 692 | ||||
| WH | Traffic management | 512 | ||||
| KLRS | Other | K | National forensic service | KA | National forensic service | 30 |
| L | Airport | LA | Airport | 31 | ||
| R | Hospital | RG | Hospital | 90 | ||
| S | Local autonomy | SA | Local autonomy | 16 | ||
| Standard job | Assigned by expert (JobExpert) | Assigned by respondents (JobSurvey) |
Automatically assigned by algorithm (JobAlgorithm) |
||
|---|---|---|---|---|---|
| No. | Agreement rate |
No. | Agreement rate (%) | ||
| Administration | 19 | 20 | 94.7 | 24 | 94.7 |
| Community safety | 37 | 26 | 62.2 | 32 | 86.5 |
| Investigation | 37 | 29 | 78.4 | 37 | 100.0 |
| Protective service | 10 | 46 | 90.0 | 13 | 70.0 |
| Intelligence security and foreign affairs | 28 | 21 | 75.0 | 23 | 78.6 |
| Academy | 2 | 0 | 0.0 | 3 | 100.0 |
| Community police | 116 | 67 | 57.8 | 115 | 99.1 |
| Mobile/special forces | 19 | 7 | 36.8 | 20 | 84.2 |
| Traffic | 25 | 22 | 84.0 | 26 | 96.0 |
| Not assigned | 8 | 50 | 0.0 | 16 | 0.0 |
| Total | 130 | 130 | 75.4 | 130 | 99.2 |
Table 1. Comparison of police personnel management system
Table 2. List of standard job codes
No. of records: number of job codes used in connection with POOL and e-Saram organizational codes.
Table 3. Comparison of standard job response results from the pilot survey with algorithm-based automatic allocation and final expert evaluation results
Agreement rate: The proportion of job allocations matched based on the assignment by expert (JobExpert).
